|
A Review of the Current Unmanned Aerial Vehicle Sprayer Applications in Precision Agriculture
Nadia Delavarpour1, Cengiz Koparan1, Yu Zhang1, Dean D. Steele1, Kelvin Betitame1, Sreekala G. Bajwa2, Xin Sun1,*
Published in Journal of the ASABE 66(3): 703-721 (doi: 10.13031/ja.15128). Copyright 2023 American Society of Agricultural and Biological Engineers.
1Department of Agricultural and Biosystems Engineering, North Dakota State University, Fargo, North Dakota, USA.
2College of Agriculture & Montana Agricultural Experiment Station, Montana State University, Bozeman, Montana, USA.
*Correspondence: xin.sun@ndsu.edu
The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0/
Submitted for review on 26 March 2022 as manuscript number MS 15128; approved for publication as a Review Article and as part of the UAVs in Agriculture Collection by Associate Editor Dr. Yeyin Shi and Community Editor Dr. J. Alex Thomasson of the Machinery Systems Community of ASABE on 9 January 2023.
Highlights
Abstract. Unmanned Aerial Vehicles (UAVs) are becoming more broadly used for improving agricultural spraying applications. However, compared with the nearly 100 years of data accumulated on manned aerial applications, UAV sprayers are relatively new, and associated technologies are in the early stages of development. The objective of this paper is to give a comprehensive review of the current UAV spraying platforms with a comparison to manned aerial sprayers and a discussion of their application, performance, and efficiency. A total of 213 peer-reviewed and non-peer-reviewed articles, extension papers, government websites, and company websites were reviewed and cited in this study. We also discuss factors that could influence the effectiveness of aerial spraying applications, such as release height, wind speed, vortex strength, and droplet size. Finally, we review the latest UAV sprayers available worldwide and present technology gaps in those platforms. We highlight areas that require improvement, particularly in autonomous navigational controllers and spraying systems.
The application of pesticides and fertilizers in agricultural domains maintains and ensures the quality and quantity of crop yield. The most common spraying methods include ground-based and aerial-based spraying systems (fig. 1). Regardless of the spraying platform, chemical substances should be sprayed and spread evenly throughout the field. It is desired to minimize skips or overlaps between sprayed areas in the field, minimize the impact of climate conditions on spraying, minimize spray drift, minimize the operator’s exposure to chemicals, and avoid damage to plants.
According to the National Agricultural Aviation Association (NAAA), aerial-based spraying applications are economical and non-destructive methods for timely pesticide application compared with ground-based platforms (NAAA, 2022a). Particularly when wet soil conditions, rolling terrain, or dense plant foliage prevent other methods of treating an area, aerial-based application may be the only remaining method of pest treatment (NAAA, 2022a).
Manned aircraft (fig. 1c) and UAVs (fig. 1d) are the two common methods for aerial application (NAAA, 2022b). However, despite the great potential of aerial-based platforms in spraying agricultural fields, each method has distinct advantages over the other, and it is crucial for growers to realize the real capabilities of each platform before making financial decisions (Freeman & Freeland, 2014).
The objective of this study is to review the application of aerial-based platforms for agricultural spraying operations. First, the performance of manned aerial sprayers versus UAV sprayers is extensively discussed. This is followed by a detailed overview of the factors that affect the effectiveness of spraying operations using UAVs. Then, the latest developments of commercialized UAV sprayers available in the market are summarized. Lastly, the main research gaps in precision spraying are reviewed, and ideas on future research directions are discussed.
Search Method
The scope of this review was limited to spraying chemicals in agricultural fields for crop protection. Initially, over 200 peer-reviewed and conference studies, research, and relevant websites were reviewed. For an automatic program script, we searched for a specific term, categories, and topics in each study, and search keywords are listed in table 1. For an article to be included in the review, at least one of the topics from the refined categories and topics was required. The manual screening was then performed by reading the abstract and the related work’s sections to reject some non-related or outdated papers. Additionally, we only surveyed references written in the English language. Finally, 100 references were selected as references for this article.
| (a) |  |
| (b) | |
| (c) | |
| (d) | |
| (e) | |
| Figure 1. Ground- and aerial-based spraying systems (not to scale): (a) knapsack sprayer, (b) tractor-mounted boom sprayer, (c) manned aerial sprayer (helicopter), (d) manned aerial sprayer (fixed-wing), and (e) UAV sprayer. | |
| Table 1. Terminology used in literature search. | ||||
| Term | Categories | Topics | ||
| Manned aerial sprayer, unmanned aerial sprayer, UAV sprayer, drone sprayer, UAS sprayer | Environmental science, agronomy, agricultural engineering, agrochemical application, plant protection | Agriculture, droplet deposition, sediment drift, droplet uniformity, spray adjuvants, pesticide, insecticide, fungicide, spray coverage, droplet density, efficiency, downwash airflow, site-specific spray, wind tunnel, nozzle, operational parameters, flight altitude, flight velocity | ||
Agricultural Aerial Applications: Manned Aircraft and UAVs Comparison
It is estimated that around 28% of all commercial cropland in the US is sprayed by conventional manned aerial aircraft (Moore, 2019). However, the statistics of aviation fatalities in agriculture indicated that agricultural pilots are at higher risk of fatal injuries when compared to pilots in other industries (CDC, 2004; Frankelius et al., 2019; NTSB, 2022). The low flying altitude of aerial agricultural applications leads to constant risks of collisions with the ground, trees, power lines, and other objects, and these occur with greater frequency than in other aviation events in nonagricultural applications (FAA, 2022b). The National Transportation Safety Board (NTSB) reported that the year 2020 had a total of 54 accidents in agricultural aerial applications, including 25 fatalities (NTSB, 2022). Therefore, it might not always be feasible or wise to fly a manned aircraft at altitudes that minimize streaking and off-target deposition.
Regardless of aerial spraying platforms, both methods include rotary-wing and fixed-wing aircraft structures (Wang et al., 2019; Yang & Pei, 2022). Considering the payload capacity and engine capability of current UAVs, conventional manned aerial sprayers (helicopter and fixed-wing) are more suitable for chemical applications over large areas of crop fields but are not economically efficient when the target area is as small as a few tens of hectares of crop fields. In these cases, considering the ease of operation, cost per hectare, and availability, UAV sprayers provide an alternative to manned aircraft in relatively smaller areas (Xiongkui et al., 2017).
UAVs for spraying operations are typically large fixed-wing aircraft with great lift and flight endurance capacity. These large UAVs require more energy and denser power sources, such as internal combustion engines and conventional fuel, to generate the required lift and endurance (Giles, 2016). By contrast, rotary-wing small UAVs’ capabilities (typically < 25 kg) to hover, autonomously travel to pre-defined locations that are difficult to access, modulate distance from the ground as topography and geography vary, create high-resolution field maps, and adjust the amount of liquid in real-time for an even coverage make them ideal for many aerial spraying operations over manned aircraft, especially spot spraying (Garg, 2021). Therefore, while rotary-wing small UAV sprayers are promising platforms for precision spot spraying, for broad agricultural and forestry chemical applications, rotary-wing large UAV sprayers (>25 kg) are in demand.
Airspace regulations vary worldwide and even between International Civil Aviation Organization member states and can limit aerial spraying operations. For example, aerial application near controlled airspace, especially near towered airports, which could extend downward to ground level, is prohibited all around the world. Still, the distance from the airport that these flights are prohibited could be different; e.g., in Spain, no flights are allowed within 8 kilometers (5 miles) from airports in uncontrolled airspace (AESA, 2022); in Australia it is 5.5 kilometers (3.4 miles) (Australian Government, Civil Aviation Safety Authority, 2022). In the U.S., the remote pilot should request authorization through Low Altitude Authorization and Notification Capability (LAANC) or FAA DroneZone (FAA, 2022d). Table 2 lists a few of the most important rules and regulations applied to aerial applications in the U.S. Due to these rules, aerial spray operations may be prohibited or tightly controlled. In many of these cases (table 2), the operator is required to obtain permission from the controlling authority for operations, if possible (Giles, 2016).
| Table 2. Rules and regulations applied to manned aerial and UAV sprayers. | ||||
| Rules and Regulations | Manned Aircraft | UAV | ||
| Pilot certificate | Commercial Pilot Certificate (NAAA, 2022d) | 14 CFR Part 107 Small Unmanned Aircraft Systems (FAA, 2022c) | ||
| Pilot’s medical certificate | Required (FAA, 2022f) | Not required (FAA, 2022c) | ||
| Required training hours for pilot | 25 hours of flight time in the make and basic model of the aircraft (FAA, 2022f) | Although no specific hours are defined by FAA, the pilot should feel comfortable operating a UAV | ||
| Flight over controlled/congested area |
| FAA authorization is required. | ||
| Wavier or exemption for agricultural spraying operations | Title 14 of the Code of Federal Regulations (14 CFR) Part 137 (FAA, 2022g; Antonelli, 2016) | Title 14 of the Code of Federal Regulations (14 CFR) Part 137 (FAA, 2022g; Antonelli, 2016) | ||
| Carrying hazardous materials | No person may carry hazardous material except Under §175.9 for aerial seeding, crop dusting, etc. (Federal Register, 2006) | Under §107.36, UAVs are prohibited from carriage of hazardous materials (ECFR, 2022a). Pilot should submit Certificates of Waiver or Authorization (COA) for carrying hazardous materials (FAA, 2022g) | ||
| Night operations | Pilots may conduct night operations under certain conditions.
| No person may operate a small unmanned aircraft system at night unless the UAV has lighted anti-collision lighting visible for at least 3 statute miles that has a flash rate sufficient to avoid a collision (ECFR 2022b) | ||
| Visual line of sight | Not required. | Required (ECFR, 2022b) | ||
| Minimum flying altitude |
|
| ||
| Maximum flying speed | No restriction. | 100 miles per hour (FAA, 2022b) | ||
| Maximum allowable payload | Depends on the hopper capacity | Total weight of UAV should be less than 25 kg (55 lb.). COA is required to fly UAVs heavier than 25 kg (FAA, 2022g) | ||
| Public notice before dispensing operations | Under §137.51(b)(2), at least 48 hours (FAA, 2022f) | Not required | ||
Although restrictive rules and regulations are applied to UAVs and manned aerial sprayers, they are not the same for both platforms (table 2). For example, in order to fly over-controlled or congested areas in the U.S., manned aircraft must take additional safety measures to ensure people and property are not at risk when conducting operations. For aircraft other than large or turbine-powered, at least one of the following inspections must be completed: a 100-hour or annual inspection within the past 100 hours in service; a 100-hour or annual inspection by a person authorized by an authorized mechanic; or an inspection under a progressive inspection system (FAA, 2022f). However, these types of inspections are not required for UAVs operating in controlled airspaces, so flying UAVs might involve less paperwork, though flying a UAV may have additional restrictions.
One additional restriction UAVs have compared with manned aircraft sprayers is that very few pesticides are labeled or permitted for UAV sprayers. Carrying hazardous materials and certain pesticide-active ingredients (e.g., allethrin, carbamate, and organophosphorus) with UAVs is not allowed (Petty, 2022; Wolf et al., 2003), so a waiver or exemption is required. In addition, an operator needs to obtain an agricultural aircraft operator certificate (14 Code of Federal Regulations (CFR) Part 137, Agricultural Aircraft Operation) in order to operate a UAV sprayer. These regulations currently restrict UAV technologies for the most common aerially applied pesticides, such as Naled, which is widely used for mosquito control (Beck et al., 2013; Delavarpour et al., 2021). It is illegal to exceed the label recommendations for pesticide application rate, making it challenging for growers to choose ultra-light volume (ULV) high-concentration spraying (Moore, 2019; Giles et al., 2018). UAV-specific labels must be created in the future to allow pesticide application where conventional manned aerial application is not feasible (EPA, 2013a, b).
| Table 3. Aerial application performance of UAV sprayers vs. manned agricultural aircraft. | ||||||
| PerformanceFactors | UAV Sprayers | Manned Aerial Sprayers | Additional Explanation | |||
| Endurance | 5-30 min (depends on the thrust-to-weight ratio and efficiency of the fuel source) (Ciobanu, 2022) | 6-30 hours (FAS, 1999) | Frequent landing and battery recharging for UAVs are required (Rabah et al., 2018). | |||
| Payload capacity to carry chemicals |
| 378-3028 L (100 to 800 gallons) (Bird et al., 1996) |
| |||
| Area coverage | Due to beyond visual line of sight (BVLOS) rules (ECFR 2022b), the covered area is limited | No BVLOS restrictions and no limitation regarding the area to cover | UAVs are more suitable for covering small areas and areas in remote locations that are hard to fly for manned aircraft (Böhler et al., 2018; Kattenborn et al., 2019). | |||
| The capability of variable-rate spraying | Still in the preliminary stages and not widely commercialized yet (Song et al., 2021) | Already developed and used (NAAA, 2022a) | Examples of variable rate manned aircraft: AG-FLOW system by Canada AG-NAV Inc., Wingman GX system by Adapco Inc., Satloc G4 system by Hemisphere Inc. (22nd Air Force, 2022; AG NAV, 2022; Satloc, 2022) | |||
| Control capability of spot spraying | Yes (Richardson et al., 2020) | No | UAVs’ abilities to hover, fly at lower altitudes, adjust flight altitude with respect to terrain, and fly at lower speeds provides a great opportunity for spot spraying (Yinka-Banjo et al., 2019) | |||
| Droplet drift and chemical losses |
|
| Although UAVs are capable of spraying lower amount of chemicals with smaller spray swaths at slower spraying speeds, which may be beneficial for reducing drift, fine and extra fine droplets could adversely affect droplet deposition and increase droplet drift (Li et al., 2019) | |||
| Ease of use |
|
| In case of UAV engine or motor failure, fault-tolerant control (FTC) can compensate for losing one of eight motors (octocopter), or even one of six (hexacopter); however, rotary-wing UAV without the FTC cannot compensate for the motor failure and will result in an accident (Chung & Son, 2020) | |||
| Cost of operation | Cost a few thousands of dollars ($1000-$20,000) (DJI, 2022a, b; Hylio, 2022; Kray Technologies, 2022; HSE-UAV, 2022) | Ranging in price from $100,000 to nearly $2 million depending on tank size, engine type, and engine size (NAAA, 2022c) | Currently, there is no standardized and accepted protocol for calculating costs per flight hour (AUVSI NEWS, 2013) | |||
[a]Fly a small UAV at night or during periods of civil twilight without anti-collision lighting require §107.29(a)(2) wavier – Operation at night (FAA, 2022b) | ||||||
Regardless of the platform, aerial application performance can be quantified in terms of endurance, payload capacity to carry chemicals, area coverage, control capability for variable rate spraying, ease of use, and cost of operation (table 3). According to the comparison between UAVs and manned aerial sprayers, UAV sprayers may be excellent tools to spray agricultural fields and create less drift than agricultural manned aircraft sprayers (Abd. Kharim et al., 2019; Guo et al., 2019; Li et al., 2021; Martinez-Guanter et al., 2020; Meng et al., 2020b; Radoglou-Grammatikis et al., 2020; Teske et al., 2018; Wang et al., 2021a; Wang et al., 2020b). UAVs may reduce drift to neighboring areas. However, due to the significant reduction in the application rate used (commonly less than 10 L ha-1), proper target coverage might be more difficult (Antuniassi, 2015). The limited payload and poor endurance of a single UAV sprayer are among the most important factors preventing farmers from widely adopting these platforms. Therefore, additional strategies, new designs, and accurate adjustments of affecting parameters (flight height, flight speed, spray pressure, etc.) are required to take advantage of UAVs’ capabilities and ensure proper spray deposition.
Considering the current technologies, the performance of UAV sprayers in real-world conditions requires further developments and studies to improve UAV performance (table 4) and surpass manned aerial aircraft performance. The lower speed in UAVs is one of the factors limiting their performance compared with manned aircraft. One of the solutions to the low speed of UAVs, limited payload, and poor endurance of a single UAV, especially when dealing with large-scale zones, could be flying different UAV clusters (McNeil et al., 2016; Mukherjee et al., 2020; Stefas et al., 2019; Teske et al., 2019). Although a combination of multiple homogeneous or heterogeneous UAVs can significantly improve the efficiency of UAV spraying, UAV clusters can introduce new challenges, such as task assignment complexities and problems of adaptive coordinated control (Li et al., 2022). The design of an efficient network architecture to control all UAVs involved in the clusters and establish reliable communication paths among UAVs is an important challenge in multi-UAV systems (Hossein Motlagh et al., 2016). The sensitivity of communication could be another limiting factor for multi-UAV sprayer systems. UAVs are preferably operated in areas without wireless networks because cellular phones and personal digital assists (PDAs) nearby may interfere with the communication between the ground station and the UAV in flight (Huang et al., 2013). Due to these contradictory situations (table 4), it is crucial to evaluate different aspects of new ideas to improve the performance of UAV sprayers and ideally customize the newly developed UAVs with new systematic designs, construction methods, sensors, and algorithms for agricultural purposes.
| Table 4. Suggestions to improve UAV sprayer applications. | ||||
| Factors Affecting the Performance | Suggestion to Improve the Performance | Drawbacks | ||
| Endurance | Improving thrust-to-weight ratio by reducing fuel-source weight, improving battery capacity and discharge rate, and propeller/motor efficiency. |
| ||
| Endurance |
|
| ||
| Payload Capacity to carry chemicals | Increasing the payload capacity of UAVs |
| ||
| Area coverage | Develop autonomous UAVs that can safely operate in BVLOS areas with limited human intervention | Requires using sensors with advanced technologies and designs, Artificial Intelligence (AI)-powered navigation and operational software | ||
| Area coverage | Change FAA regulations regarding BVLOS |
| ||
| Capability of variable-rate spraying | Incorporation of technologies including real-time weather monitoring, flow control, in-flight boom length reduction systems for drift reduction, and pulse width modulation technology to provide flow, pressure, and on/off control for each individual nozzle (Campos et al., 2019; Wen et al., 2019) |
| ||
| Control capability of spot spraying | Development of a single UAV sprayer with the capabilities of detection and mapping of the pest-infested area as well as precision spraying (Hunter et al., 2020) | Flying with a heavy payload (i.e., a tank with pesticide solution) and seeking weeds would significantly reduce the limited battery life | ||
Working Effectiveness of UAV Sprayers
UAVs could reduce chemical pesticide and fertilizer application by 15%–20% by using a low or ultra-low spray volume, thereby reducing the amount of chemicals penetrating groundwater (Mazur, 2016). Regardless of the spraying platform, the effectiveness of spraying operations can be quantified in terms of droplet drift, penetrability, droplet deposition, droplet size uniformity, and droplet distribution uniformity. In this study, the droplet disposition uniformity is characterized as the coefficient of variation (CV) of droplet deposition (Smith, 1992); the smaller the CV values, the better the uniformity of the droplet deposition. Droplet coverage is also defined as the ratio of the area covered by droplets to the analyzed zone on water-sensitive paper (Cunha et al., 2012). Many variables affect the efficiency of spraying operations, including forward speed, air temperature, humidity, wind speed and direction (Qi et al., 2020), atmospheric stability, terrain condition, spraying dosage, spraying equipment, application rate, nozzle type, nozzle angle, nozzle height, spray pressure, liquid volatility, and viscosity.
Under fixed operating parameters, such as flight altitude, flight speed, and spraying liquid, the effectiveness of a spraying operation is mainly affected by weather conditions (Wang et al., 2018b). Changes in wind direction and speed during spray application tend to greatly affect spray uniformity, pattern, distribution, size, and coverage (Fritz, 2019). Wind can easily disrupt a spray pattern, cause the material to stack on the upwind side, and result in a narrow-deformed spray pattern. Although wind has the largest impact on spraying efficiency, the importance of other factors is undeniable (table 5). It should be noted that table 5 is the result of the search outlined in table 1. The result of this review study shows that the factors that affect UAV sprayer applications include UAV sprayer type, operational flight parameters, nozzle type; spraying flow rate, droplet size, and spraying volume; pesticide dosage and spray adjuvants; canopy morphology; and temperature inversion. For each reviewed study, information regarding the considered variables, measured parameters, operational parameters, nozzle type, and the sprayed target was collected. We summarize our findings in the following subsections.
| Table 5. List of literature articles included in this review and details of each article. | ||||||||||
| Considered Variables | Measured Parameters | Operational Parameters | Nozzle Type | Sprayed Target | Ref | |||||
Operational parameters | Spray deposition, the number of spray deposits, coverage, and droplet size | Makeflyeasy Freeman 2000. Flight altitude= 2 and 3 m Flight velocity= 2 and 3 m/s | Nine TeeJet110055 nozzles equally spaced (50 cm). Flow rate= 4.4 L/min | Wheat field | (Ahmad et al., 2020) | |||||
Operational parameters | Droplet coverage and deposition uniformity | HyB-15L. Flying altitude= 0.5-1.5 m Flight velocity= 0.3-5 m/s | Four TeeJet110055 nozzles equally spaced (45 cm). Flow rate= 0.28 L/min | Rice canopies | (Qin et al., 2016) | |||||
Flight altitude and spraying dosage (270, 360, 450 g/ha) | Droplet deposition | DJI N3. Flying altitude= 3 and 5 m Flight velocity= 4 m/s | Two rotary atomizers. Flow rate= 0.85 L/min | Wheat field | (Qin et al., 2018) | |||||
Flight altitude (1.5 and 2 m) | Droplet distribution and drift, control efficiency | Jifei P20 UAV. Flying altitude= 1.5 and 2 m | - | Cotton | (Lou et al., 2018) | |||||
Meteorological conditions | Downwind drift and in-swath deposition | QuanFeng12. Flight altitude= 1.5, 2.5, 3.5 m Flight velocity= 3 m/s | Two 120-02 nozzles. Flow rate= 0.8 L/min | Pineapple orchard | (Wang et al., 2018b) | |||||
Nozzle type and size, flight speed, adjuvant (DRS-60, Y-20079, MF and G-611) and meteorological parameters (20°C & 40%, 20°C & 80%, 30°C & 40% and 30°C & 60%) | Airborne and downwind drift | Quanfeng 3WQFTX-10. Spraying height= 1 m Flight velocity= 2 and 5 m/s | Flat fan: ST 110-0067, ST 110-015, ST 110-02, XR 110-01, XR 110-03. Hollow cone: TR 80-01, TR 80-0067. Air-inclusion: IDK 90-0067, IDK 120-01, IDK 120-015 | Wind tunnel experiments | (Wang et al., 2020a) | |||||
Droplet size (100, 150 and 200 µm) Ambient wind speed (0-3.81 m/s) | Deposition in-swath and downwind drift | XAG P20. Flight altitude= 4 m Flight velocity= 5 m/s | Four centrifugal atomization nozzles. Flow rate= 0-0.675 L/min | Flat grassland | (Wang et al., 2020b) | |||||
Droplet size (95.21, 121.43, 147.28, and 185.09 µm) | Droplet deposition distribution, droplet penetration, and drift | DJI MG-1S. Flight altitude= 1.5 m Flight velocity= 5 m/s | Teejet 11001VS, Teejet 110015VS, Teejet 11002VS, and Teejet 11003VS. Flow rate= 0.7 L/min | Rice canopies | (Chen et al., 2020b) | |||||
UAV flying speeds, and spraying rate (0.75, 1.5, 2.25 & 3.00?L/min) | Droplet deposition density | AgFarm UAV. Flight altitude= 2 m Flight velocity= 2, 4, and 6?m/s | Fan shaped, Green, F110-015, Flow rate= 0.75, 1.5, 2.25 and 3.00?L/min | Rice canopy | (Abd. Kharim et al., 2019) | |||||
Pesticide dosage (72, 90 g/ha), and spraying adjuvants (organosilicone QF-LY and methylated vegetable oil FFD) | Droplet deposition, droplet density, coverage, control effect and pesticide residue from field trials | Quanfeng 3WQF120-12. Flight altitude= 0.5-3 m Flight velocity= 1-7 m/s | Flow rate= 0.0002 L/min | Wheat | (Meng et al., 2018) | |||||
Dosage and Spraying Volume | Defoliants Efficacy | JT-30 UAV. Spraying height= 2 m Flight velocity= 5 m/s. 3WQF120-12 UAV. Spraying height= 2 m Flight velocity= 4 m/s | Six hollow conical nozzles, Flow rate= 0.68 L/min. LU120-01/LU120-02/LU120-02/LU120-04, 0.3/0.56/0.77/0.93 L/min | Cotton | (Xin et al., 2018) | |||||
Spray volumes (9.0,16.8, and 28.1 L/ ha) using three different nozzle sizes | Droplet deposition and wheat aphid and powdery mildew control | Quanfeng 3WQF120-12. Flight altitude= 2 m Flight velocity= 5 m/s | LU120-01, LU120-02, LU120-03. Flow rate= 1.5-1.7 L/min | Wheat | (Wang et al., 2019) | |||||
UAV Type
The type of UAV could be considered one of the factors that influence the efficiency of spraying applications. Among the different UAV types used for spraying purposes, fixed-wing UAVs are more prone to drift to nearby areas (Delavarpour et al., 2021). The use of rotary-wing UAVs, typically flown at 3 to 5 m above the crops, has been assessed as safer with higher precision compared to fixed-wing UAV sprayers for chemical spraying (Bae & Mo Koo, 2013; Faiçal et al., 2014b; Huang et al., 2009; Wang et al., 2018b). Various types of rotary-wing UAV sprayers currently available in the market are shown in figure 2. Spraying at lower speeds and altitudes by rotary-wing UAVs provides enough time to deliver the chemical to the target surface. Additionally, the downwash effect generated by the UAV’s propellers could help deliver the droplets onto the target surface more efficiently than fixed-wing sprayers (Hanson, 2008) due to the vortex created under the UAV by the propellers, which causes droplets to aggregate on the ground. However, flight direction, height, and crosswind could significantly weaken the intensity of the downwash air?ow field. Gradual decrease in the tank payload lead to changes in the downwash wind field, which may affect droplet distribution (Zhan et al., 2022).
A weak downwash airflow reduces the effectiveness of the downwash effect on droplet deposition and increases pesticide drift to neighboring areas (Guo et al., 2019; Wang et al., 2018a). It might help transport droplets to the ground in volume, but it reduces droplet uniformity due to aggregation when compared to traditional sprayers. According to table 6, due to several capabilities of rotary-wing UAVs, including hover and vertical take-off and landing (VTOL), these platforms are gaining more attention.
Operational Parameters
Operational parameters, including flying altitude and velocity, significantly impact UAV spraying operations using UAVs (Moore, 2019; Qin et al., 2016). Lower flying velocity and the altitude might improve droplet coverage; however, it is important to consider the effect of these factors on the uniformity of droplet distribution and adjust both factors to reach the maximum possible efficiency (Tang et al., 2018; Zhang et al., 2020). Martin et al. (2019) discovered that an aerial application with an octocopter DJI Agras MG-1 at 3 m altitude and 5 m/s flight speed with four nozzles placed right beyond the rotors resulted in a similar droplet VMD (Volume Median Diameter) compared with a hexacopter V6A UAV at 2 m altitude and 1 m/s flight speed with four nozzles placed on a vertical boom. VMD is the droplet diameter where 50% of the spray volume is contained in droplets smaller than this value. A larger VMD may impose a lower risk for drift (Zhang & Xiong, 2021). The optimum UAV operational parameters should be reasonably selected according to the weather conditions, including wind speed (Wang et al., 2018b). Faiçal et al. (2014a) proposed a methodology to reduce the drift of pesticides by connecting the UAV spray system to a wireless sensor network to send feedback on the weather (e.g., speed and direction of the wind) and adjust the concentration of the pesticides sprayed on the crops accordingly. Based on the feedback received, the UAV appropriately applied an algorithm to correct its route in the direction with lower drift. According to these authors, this methodology increased the precision of spraying pesticides in such a way that approximately 86% of the product reached its target, while in many cases of aerial spraying, less than 50% of the chemical spray deposits on the target plant or pest (Faiçal et al., 2014a; Willis & McDowell, 1987). According to data collected from reviewed articles, the most commonly tested flight altitude range is 1-5 m above plants at a flying velocity of 2-5 m/s (table 5).
Nozzle Type
For spraying, a well-performing nozzle can greatly improve the spray uniformity across the field, ensure uniform droplet distribution, reduce drift, and enhance pesticide utilization efficiency (Sumner, 2012; Zhang et al., 2014). The operating pressure, spraying angle of nozzles, and nozzle tips greatly impact the performance of nozzles, such as droplet penetrability, droplet size, and flow rate. Different nozzle types are designed for use under certain application conditions. The most common nozzle types include flat-fan, even flat-fan, and cone nozzles. Each nozzle type has specific characteristics and capabilities and is designed for use under certain application conditions. Regular flat-fan nozzles are used when foliar penetration and coverage are not required (Gil et al., 2014). The higher the operating pressure, the finer the drops produced, and the possibility of drift increases significantly. High pressure should be used only to apply foliar pesticides that must penetrate the plant canopy or require maximum coverage (Sumner, 2012). Even flat-fans are used for banding chemicals over the row and applying uniform coverage across the entire width of the spray pattern. In even flat-fans, the width of the band depends on the nozzle height. When drift is not a major concern and plant foliage penetration is essential for effective insect or disease control, cone nozzles are the best option (Torrent et al., 2019). These nozzles are preferably used to penetrate plant canopies and cover the underside of the leaves at high pressures of 40 to 80 psi. However, since at high pressures droplets are very susceptible to drift, cone nozzles should be avoided for broadcasting herbicides or any chemical where drift can cause a problem (Sumner, 2012). The characteristics of nozzles should be selected carefully with respect to the spraying application. For example, the correct nozzle tip size depends on the application rate in liters per hectare, ground speed, and effective spray width of each nozzle (Price, 2018; Sumner, 2012).
The current USDA-ARS Aerial Spray Nozzle Models are divided into two distinct types: high-speed fixed-wing aircraft and low-speed fixed-wing and rotary-wing aircraft (NAAA, 2022e). The higher-speed models are applicable for airspeeds ranging from 120 to 180 mph, while the lower-speed models are applicable for airspeeds ranging from 50 to 120 mph. However, there is no recommendation regarding the current rotary-wing UAV sprayers capable of flying as slow as 3-6 mph (JMRRC Drone Store, 2022). Also, due to their flying capability at very low altitudes, the downward airflow created by rotary-wing UAV sprayers could significantly impact the effectiveness of the spraying operation. This is another strong reason that a nozzle recommendation for rotary-wing UAV sprayers is required to consider the flight altitude, speed, the downward force generated by propellers, crop leaf type, and weather conditions.
 |  |  |
| Ag Cormorant (Tactical Robotics LTD., 2022) | DJI-Agras MG-1 (Abbie, 2018) | Agridrones B2B2C (Agridrones Solutions Israel, 2017) |
 |  |  |
| Drone Volt-Hercules 20 (Flydragon, 2019) | Shenzhen GC Electronic- JMR-X1380S (Flydragon, 2019) | XAG-P40 (DroneBlogger, 2021) |
 |  |  |
| Yamaha- RMax (Gallagher, 2015) | DJI- T20 (PR Newswire, 2020) | DJI- T16 (Vietnam Pepper, 2021) |
 |  |  |
| THANOS-SYENA-Q10 (THANOS, 2022) | Kisan Crop Duster (OM UAV Systems, 2022) | EFT E616S Drone (RDRONGUY TECHNOLOGY, 2022) |
 |  |  |
| KRAY protection (Kray Technologies, 2022) | Optim (Atherton, 2016) | Volocopter. Credit: JohnDeere / Volocopter |
 |  |  |
| HSO-M8APro (HSE-UAV, 2022) | Yamaha-Fazer R (Yamaha, 2022a) | Hylio-AG-122 (Hylio, 2022) |
| Figure 2. Various types of UAV spray drones currently available in the market. | ||
Because of the small tank size, the main usage of this UAV in the U.S. is spraying herbicide on levees, fence rows, hill sides, ravines, swamps, wetlands, right-of-way easements, and horticultural areas. Even these spraying applications require a spray system with low drift qualities to avoid damaging susceptible crops, plants, gardens, and other foliage nearby. According to a study conducted by Louisiana State University Extension on the main nozzle types—air induction (A.I.), 80-degree flat fan, Turbo TeeJet, Turbo TwinJet, Drift Guard (DG), hollow cones, and XR flat fans—XR flat-fans create more drift than most of the other nozzle types, and A.I. nozzles create the smallest amount of drift at operating pressures of 40 psi (Price, 2018). Priceet al. (2019) studied nozzle setting selection for drift reduction in the DJI AGRAS MG-1/1S sprayer and recommended nozzle sizes and types for maintaining a good droplet size for low-volume applications of less than 2 GPA (gallons per acre). In this study, air induction (A.I.) flat fan nozzles, including Greenleaf AM11001, TeeJet AIXR110015, and AIXR11002 were selected for testing on UAV sprayers. The result of the study conducted by Price et al. (2019) showed that flat-fan nozzles greatly reduced drift by up to 60% compared with standard XR11001 nozzles. The droplets from AIXR110015 and AIXR11002, were too coarse for low-volume applications (less than 2 GPA), and the AM11001 nozzles with a 0.01 GPM orifice size had a much better droplet spectrum for low-volume applications (less than 2 GPA). Thus, the Greenleaf AM11001 nozzles were recommended for use on this UAV.
| Table 6. Types of UAV sprayers in some of the most recent studies reviewed in this article. | ||
| Reference and Year of Publication | UAV-Model | UAV Type |
| Xue et al., 2016 | N3 helicopter | Rotary-wing |
| Qin et al., 2016 | HyB-15L UAV | Rotary-wing |
| Qin et al., 2018 | N3 helicopter | Rotary-wing |
| Wang et al., 2018b | QuanFeng120 UAV, Anyang QuanFeng Aviation Plant Protection Technology Co., Ltd., Henan, China | Rotary-wing |
| Meng et al., 2018 | 3WQF120-12, Quanfeng, China | Rotary-wing |
| Xin et al., 2018 | JT-30 UAV 3WQF120-12 UAV | Rotary-wing |
| Guo et al., 2019 | UAV developed in the National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology of South China Agricultural University, Guangzhou, China | Rotary-wing |
| Abd. Kharim et al., 2019 | Eight multi-rotor AgFarm | Rotary-wing |
| Wang et al., 2019 | 3WQF120-12, Anyang Quanfeng Aviation Plant Protection Technology Co., Ltd., Xinxiang, China | Rotary-wing |
| Chen et al., 2020b | MG-1S eight-rotor, Shenzhen DJI Technology Co., Ltd., ShenZhen, China | Rotary-wing |
| Chen et al., 2020a | XAG P30, XAG Co., Ltd., Guangzhou, China | Rotary-wing |
| Tian et al., 2020 | XAG P20 UAV sprayer | Rotary-wing |
| Ahmad et al., 2020 | Freeman-200 model, Feirui Company, Zhenjiang, China | Rotary-wing |
| Wang et al., 2020b | P20, XAG Company, Guangzhou, China | Rotary-wing |
| Guo et al., 2020 | ZHKU-0404-01 quad-rotor | Rotary-wing |
| Gao et al., 2020 | XAG P20 UAV sprayer DJI T16 UAV | Rotary-wing |
| Song et al., 2021 | R20, Xiangnong Innovation Technology Co., Ltd., Shenzhen, China | Rotary-wing |
| Wang et al., 2021b | DJI M600 six-rotor UAV | Rotary-wing |
| Wang et al., 2021a | 3WQF120-12, Anyang Quanfeng Aviation Plant Protection Technology Co., Ltd., Henan, China 3WM6E-10, TT Aviation Technology Co., Ltd., Beijing, China 3WM8A-20, TT Aviation Technology Co., Ltd., Beijing, China | Rotary-wing |
| Chen et al., 2021a | XAG P series UAV, XAG Co., Ltd, Guangzhou, China | Rotary-wing |
| Biglia et al., 2022 | DJI Matrice 600 Pro, DJI, China | Rotary-wing |
| Hao et al., 2022 | XAG P20, Xaircraft, China | Rotary-wing |
| Zhan et al., 2022 | UAV P20, XAG Co., Ltd, Guangzhou, China | Rotary-wing |
| Cavalaris et al., 2022 | TopXGun T416 | Rotary-wing |
| Zhang et al., 2022 | DF-16L, Henan Difengde Aviation Technology Co., Ltd., Henan Province, China | Rotary-wing |
| Hussain et al., 2022 | AGRAS MG-1P, SZ DJI Technology Co., Ltd., China | Rotary-wing |
| Yu et al., 2022 | T16, DJI Technology Co., Ltd., Shenzhen, China XP2020ST, XAG Co., Ltd., Guangzhou, China | Rotary-wing |
While several studies contain recommendations on how to select the most appropriate nozzle type and nozzle characteristics based on the desired application for ground based and manned aerial spraying applications (Nowatzki, 2009; Ozkan, 2016; Price, 2018; Hofman & Solseng, 2017; Peters et al., 2017; Sumner, 2012), there is a lack of nozzles designed for UAV applications, so nozzle selection for UAV plant protection often reverts to ground-based or manned aircraft sprayer standards. Though there are not many differences regarding the nozzle type between UAVs and manned aircraft, UAV capabilities make it feasible to achieve better droplet deposition at a specific location in the field compared with manned aircraft in the same spraying conditions (Yao et al., 2017; Zhang et al., 2018; Zhang et al., 2014, 2015). Specifically, because of the capabilities of flying at low altitudes and the ability to hover, a downwash airflow is generated by the UAV rotors that can interact with the crop canopy and form a conical vortex shape in the crop canopy (Guo et al., 2019; Wang et al., 2018a). Different vortex states indicate different levels of interaction between the wind field and the canopy. In these cases, a higher amount of deposition in the downwind direction can be expected. Due to these capabilities of rotary-wing UAVs, further studies are required to specify the nozzle settings for UAVs regarding the desired application to achieve the maximum droplet deposition.
Spraying Flow Rate, Droplet Size, and Spraying Volume
Spraying flow rate, droplet size, and spray volume affect the droplet deposition density and uniformity of spray deposition. High flow rates at low flying altitudes and velocities have improved droplet deposition density and spray deposition uniformity (Abd. Kharim et al., 2019). An experimental platform was developed by Wang et al. (2021b) to evaluate the spraying quality of a UAV sprayer under indoor conditions and predict the optimal values for four spraying parameters: spraying height, flow rate, distance between nozzles, and pulse width modulation (PWM) duty cycle. In this study, the authors used centrifugal-type nozzles and realized that the PWM duty cycle and flow rate had the greatest impact on the quality of spraying, followed by nozzle distance and spraying height. The optimal values for each parameter were suggested as 98.65%, 1.74 L/min, 1.0 m, and 1.60 m, respectively. Moreover, droplet drift depends heavily on the droplet size. Ultra-low volumes used in the UAVs imply a fine droplet atomization that is prone to drift or evaporation (Hobson et al., 1993). The smaller the droplet, the longer it floats in the air, allowing wind to carry it away more easily (Chen et al., 2020b). Ferguson et al. (2018) showed that coarse sprays provide the most efficacy across a wide array of modes of action and reduce spray drift potential compared to finer sprays. However, some herbicides are less effective when sprays are too coarse because droplets may not be deposited on target crop surfaces or intercepted by target leaves, especially for crops and weeds with small, narrow leaves (Ferguson et al., 2018).
The droplet size also affects the droplet deposition rate and penetration in the plants' upper and lower portions. Chen et al. (2020b) showed that the cumulative drift rate and the drift distance of droplets decreased with increased droplet size, which indicated that the increase in droplet size could effectively reduce droplet drift. Wang et al. (2019) compared droplet deposition under different spray volumes (9.0, 16.8, 28.1 L/ha) and droplet sizes (LU120-01, -02, -03). The result demonstrated that a low spray volume of 9.0 L/ha with a fine droplet size (nozzle 50~150 µm) resulted in a lower deposition. They indicated that better control of wheat diseases and insect pests was achieved using coarse droplet size (>150 µm) and higher spray volume (>16.8 L/ha).
Pesticide Dosage and Spray Adjuvants
Pesticide dosage, along with spray adjuvants, is a primary factor affecting the retention period of spraying (Beck et al., 2013; Wang et al., 2020a; Wolf et al., 2003). In general, the use of UAVs in spraying applications could be efficient when used with high-concentration and ultra-low-volume (ULV) doses. However, the fine and easily transportable droplets that can provide excellent crop coverage and effectiveness for ULV treatments are the same factors that can present a significant risk of pollution through drift (Anand & Goutam, 2019). Also, application accuracy is important in ULV spraying because spray overlap can lead to phytotoxicity, while gaps in the spray pattern can lead to poor pest control. Qin et al. (2018) studied the effect of spraying dosage (270, 360, 450 g/ha) on pest control efficiency after spraying using UAVs. Although the authors of this study reported that the efficiency of the lowest dosage was not significantly different from the higher dosages seven days after pesticide application, the control efficiency of the 360 and 450 g/ha dosages was higher than the lowest dosage ten days after the experiment. This research showed that the retention period of the highest dosage was longer than that of the lower dosage (Qin et al., 2018).
Although a lower spraying dosage could result in lower droplet deposition in all layers, an auxiliary agent (adjuvant) can be used to prolong the pesticide prevention effect on the crop surface. Applying adjuvants to the lower dosage could significantly improve droplet deposition (Meng et al., 2018). Thus, it is possible to reduce the dosage of the chemical and still achieve good efficiency in the operation by applying adjuvants. Adding an adjuvant also improves the control efficacy period of UAV spraying, even with reduced pesticide dosage (Meng et al., 2018). Adjuvants can also minimize droplet drift by controlling the spray liquid's physical and chemical properties, including wetting, spreading, adhesion to the target, reducing evaporation, volatilization, and spray drift (Elsik et al., 2010).
In addition to reducing the spraying drift, adjuvants can also reduce evaporation, foaming, and volatilization. It would be difficult to perform all these functions only with a single adjuvant, and different types of adjuvants should be combined to achieve multiple functions. There are two types of adjuvants:
- Formulation adjuvants that are already included as part of the pesticide product by the manufacturer when it is made (Hock, 2022)
- Spray adjuvants are separate products that are added to a pesticide spray solution by the applicator (Hock, 2022).
Since adjuvants do not control pests, they should be registered by the U.S. Environmental Protection Agency (EPA) only if they are to be used on a food or feed crop to establish tolerance or tolerance exemption for the adjuvant (Zhang & Xiong, 2021). Moreover, although adjuvants do not have any pesticidal activity, they are chemically and biologically active products that can pose health risks (Hock, 2022). While UAV pesticide regulations restrict various pesticide dosages, current pesticide labeling of the United States Environmental Protection Agency (US EPA) and drift modeling does not include small rotary-wing UAVs (Rau, 2021; EPA, 2014). Moreover, recommendations are still not well developed for high-efficiency adjuvants for UAV application, especially for low or ultra-low-capacity spraying (Chen et al., 2021b). This restriction potentially reduces the UAV spraying operation's efficiency in the lower canopy's lower layers.
Canopy Morphology
A uniform spray distribution on crop leaves is influenced by the canopy morphology. Leaf area index (LAI), canopy height and width, and canopy shape can affect the spray deposition as a ratio between the actual and expected deposit (Campos et al., 2019). Liao et al. (2020) showed significant correlations between LAI and spray quality and efficacy in cotton defoliant spraying using three different types of rotary-wing UAVs. Meng et al. (2019) discovered that UAV spraying of harvest aids resulted in poor penetration in sections with a high density of plants and plants with high LAI. With current technology, droplets are more likely to be distributed in the upper part of the canopy than the lower part (Chen et al., 2020a). Tang et al. (2018) investigated the effects of different tree shapes on the droplet deposition distribution using UAV sprayers for inverted triangle-shaped and triangle-shaped citrus trees at flight elevations of 0.6?m, 1.2?m, and 1.8?m. This study discovered a significant difference in the droplet density and droplet coverage rate between the upper and lower layers of the inverted triangle-shaped trees. However, there was no statistically significant difference between the middle and lower layers between the droplet density and droplet coverage rate. Compared to that of triangle-shaped trees, the droplet density of the lower layer of the inverted triangle-shaped trees increased by 36.54%, 48.04%, and 146.49% as flight height increased. The droplets exhibited the most uniform distribution (CV?=?32.44%) in the lower layer of the inverted triangle-shaped trees. To optimize the spray application efficiency and reduce environmental contamination, a continuous adjustment of the application rate is required for different shapes, sizes, and densities of foliage in crops during the same growing season.
Temperature Inversion
Temperature inversions without air convection near the ground cause droplets to float in the air and can facilitate physical droplet drift and vapor drift (Enz et al., 2019). Aerial spraying during a temperature inversion may increase the lateral movement of fine drops and pesticide vapor. Temperature inversions could greatly impact the efficiency of all aerial applications and should be avoided when using a fixed-wing aircraft (Fritz et al., 2008). In rotary-wing aircraft, strong downwash generated by rotors could provide the required kinetic energy for droplets to drop on leaves and not float in the air, which can mitigate the effect of temperature inversion (Tian et al., 2020). However, excessive downwash flow might cause turbulence and uneven droplet distribution on individual leaf surfaces.
Temperature inversion usually happens at night, which is theoretically favorable for pesticide application by rotary-wing UAVs due to the stable atmosphere that mitigate droplet drift and the lower temperatures and higher relative humidity at night that reduce droplet evaporation (Fritz, 2006; Fritz & Hoffmann, 2008). Researchers at Stoneville collected meteorological data and analyzed the daily atmospheric stability change and the likelihood of surface temperature inversion (Core, 2005; Huang, 2019). Using this data, an algorithm was developed for a website, which was published to guide aerial applicators on the timing to conduct spray application to avoid off-target drift caused by temperature inversion (Huang & Thomson, 2016; Thomson et al., 2017). This study and the similar one conducted by Huang & Fisher (2019) were conducted using the manned aircraft sprayer Air Tractor 402B (Air Tractor Inc., Olney, Texas, USA). Similar studies are required to determine the best timing to conduct aerial spraying applications, considering the differences between aerial spraying applications conducted by UAV sprayers and manned aerial applications.
Current Commercialized UAV Sprayers
A UAV sprayer should be able to work in the most complicated environments and on different agricultural lands. Several companies manufacture agricultural UAV sprayers with different technologies incorporated to improve the accuracy of operations, from easy autonomous flight planning and terrain-sensing radar to extended flight time, high payload capacity, and off-the-grid power options. However, due to the lack of standard protocols for UAV development for agricultural applications, it is difficult to decide which UAV is appropriate for any single project, both technically and economically (Huang et al., 2013). The most recent products of different companies and their specifications are listed in table 7 (images of these products can be found in fig. 2). All of these UAV sprayers are built on rotary-wing aircraft and include the following features:
- Vertical-take-off-and-landing capability, which reduces the turnover time
- Manual and autonomous flying modes
- Manual and autonomous mission planning
- Manual and automated crop spraying
- Terrain following and height adjustment
- Obstacle detection and collision avoidance
- Return home in case of emergencies (e.g., low battery power or liquid amount)
- Carbon fiber airframe structure material
- IP67 rated core modules
- Flying at very low altitudes (1-5 m)
- Flight controller kit (flight controller, GPS, power module, radio link)
- Reading data and maps collected by another UAV for autonomous mission planning
Currently, most UAV sprayers available on the market are designed for aerial broadcast applications in autonomous mode. While these UAV sprayers can cover entire fields, conducting site-specific and spot spraying within a field can be more cost-effective if not all areas require coverage. However, there are currently no commercial UAV sprayers that possess the ability to identify target areas, adjust spray rates based on plant needs, or perform real-time spot spraying. To achieve site-specific and spot spraying, two complementary systems are required on a UAV: (1) real-time region of interest detection, such as identifying pest-infested areas, and (2) precision spraying capability (Zang et al., 2016).
Ideally, these two systems should be in a single UAV. However, except for OPTiM Agri Drone (Atherton, 2016), all the reviewed UAV sprayers require mapping the area in advance. The rotary-wing OPTiM Agri Drone (Atherton, 2016) is a joint project among Saga University, Saga Prefecture Government, and IT company OPTiM, to optimize UAVs for agricultural work and is currently being tested in Japan. This UAV has multi-spectrum image analysis capabilities that help identify pockets of insects and can then do a precise, pinpoint attack on those areas with pesticides or other chemicals. Unfortunately, the company has not shared further details regarding the technology used on this UAV and has not quantified the success rate of their product yet. Thus, despite the potential of UAVs for site-specific spraying applications, there is little information on the targeting accuracy of UAVs configured for spot treatments.
| Table 7. Commercial UAV sprayer specifications. | |||||||
| UAV Sprayer | Maximum Payload (L) | Maximum Spraying Rate (L/min) | Spray Width (m) | Endurance Time (min) | Maximum Operating Speed (m/s) | Maximum Wind Resistance (m/s) | Area coverage (hectare/hour) |
| Ag Cormorant (Grassi, 2019) | 500 | -[a] | - | - | - | - | - |
| DJI-Agras MG-1 (DJI, 2022c) | 10 | 0.43 | 4-6 | 10-24 | 8 | - | 7-10 |
| Agridrones B2B2C (Agridrones Solutions Israel, 2017) | - | - | - | - | - | - | - |
| Drone Volt-Hercules 20 (Veille, 2017) | 12 | 3 | 3 | Max 40 | 25 | - | 10.8 |
| JMR-X1380S (JMRRC Drone Store, 2022) | 10 | 84-108 | 3-6 | 10-15 | 3-6 | - | 13-17 |
| XAG-P40 (XAG, 2022) | 20 | 10 | 6 | - | 5 | - | 15 |
| Yamaha RMAX (Yamaha, 2022b) | 16 | 1.3-2 | - | - | - | - | 486 |
| DJI-AGRAS T20 (DJI, 2022b) | 20 | 6 | 7 | - | 7 | 8 | 12 |
| DJI-AGRAS T16 (DJI, 2022a) | 16 | 4.8 | 6.5 | - | 7 | 8 | 10 |
| THANOS-SYENA-Q10 (THANOS, 2022) | 10 | - | 3-5 | 20 | 10 | - | 12 |
| Kisan Crop Duster (OM UAV Systems, 2022) | 10 | - | 3-4 | 20 | 5.5 | 8.3 | - |
| EFT E616S Drone (RDRONGUY TECHNOLOGY, 2022) | 16 | - | 3-5 | - | - | - | - |
| KRAY protection (Kray Technologies, 2022) | 33 | 0.01-5 | 5 | 22 | 25 | - | 27-47 |
| OPTim Agri Drone | - | - | - | - | - | - | - |
| Volocopter (Malewar, 2019) | 200 | - | - | 30 | - | - | 6 |
| HSE, M8A PRO (HSE-UAV, 2022) | 20 | - | 9.1-10.6 | 12-15 | 10 | 12 | 11-14.5 |
| Yamaha Fazer R (Yamaha, 2022a) | 32 | 1.3-2 | - | 100 | 20 | - | - |
| Hylio, AG-122 (Hylio, 2022) | 20 | 0.4-7.6 | 6.1-9.1 | 15 | 9.8 | - | 12-14 |
[a]Data not available for "-" entries. | |||||||
Prior pest mapping provides an optimal path to the pest-infested areas for spraying and maximizes UAV sprayers’ limited flight times and payload capacities. However, using separate UAV platforms for mapping and spraying can be time-consuming because it requires the UAV to collect data (video and /or still image), compress the collected data using a standard compression technique like JPEG, Wavelet, or MPEG, transfer the data through a wireless link to a ground station, analyze the collected data, create a site-specific pesticide application map to support path planning for automated systems, upload the map to the UAV sprayer controller, and finally spray the field according to the map. Image/video compression is computationally heavy and requires intensive power consumption. Moreover, sufficient bandwidth is required for wireless transmission of compressed image/video data, which puts additional pressure on system power resources (Ehsan & McDonald-Maier, 2009). Data received at the ground station is generally noisy and delayed (Tippetts, 2008).
Tele-operation, which is operating a robotic device from a distance over a wireless network from a centralized location, is usually used for small UAVs and cannot guarantee near real-time operation (Kangunde et al., 2021) when considering UAV aerial spraying. In contrast to the teleoperation method, the Onboard vision processing method carries out the vision processing and map generation on board (Isop et al., 2019). With the currently available technology, this method is practically feasible only for simple image processing operations to keep system power consumption within reasonable limits (Ehsan & McDonald-Maier, 2009).
The current state of this technology presents several challenges to its applicators, including the robust applicability of developed techniques across different scenarios, the impact of camera parameters and flight parameters, computation burdens, the capability of different devices for UAV flight control, and the detection of unknown pest species (Mohidem et al., 2021). Therefore, further studies are needed to develop user-friendly systems that automate pest detection and mapping, as well as the generation of prescription application maps. These advancements will facilitate the adoption and utilization of UAV sprayers (Meng et al., 2020a; Wang et al., 2021a). Additionally, investigating the utilization of UAV sprayers on a large scale, such as in agriculture and construction applications, is crucial. In the construction sector, for example, conventional methods like water curing in cement-based materials (Harirchi and Yang, 2022),and reducing heat stress can be potentially improved through the use of UAVs (Hassandokht Mashhadi et al., 2022).
In addition, the use of full or semi-autonomous UAVs to perform the spraying operation has not efficiently addressed the problem of how to autonomously determine and control the flying and spraying parameters and continuously adapt the flight route of a UAV spraying pesticides in a highly dynamic environment. In semi-autonomous operation, a UAV must be able to adjust its flight route according to its velocity, operation height, the orientation of the wind, and the type of chemical being sprayed, as this might change the droplet size, count, and coverage. The current commercialized UAVs are not allowed to fly BVLOS because none of the navigation and positioning systems on these UAVs are sufficiently reliable to prove to aerospace regulators that they are capable of safe and accurate independent navigation, especially when yielding the right of way is necessary. However, a second consideration is required for rotary-wing UAVs that are capable of flying, taking-off, and landing safely at very low altitudes where no aerial aircraft are allowed to fly. Thus, there is a strong need for further studies to precisely consider all the capabilities of UAVs, ensure that the UAV follows its intended route, and facilitate the legalization of fully- or semi-autonomous UAV flights.
Conclusion
In the last decade, UAVs have been extensively used in agriculture. However, several arguments question the operational efficiency of these platforms since UAV droplet deposition rates, leaf coverage of chemicals, and absorption are slightly inferior to those obtained through other spraying methods. The overall efficiency of the UAV spraying, such as chemical volume, droplet deposition, and leaf absorption, is still not as high as desired, and chemical loss to the environment remains an issue. Moreover, many practical and technical issues are associated with UAV spraying for plant protection, such as ambiguity in optimal operational parameters, poor penetrability into the crop canopy, a low droplet coverage ratio, and non-uniform droplet distribution. Operational limitations of UAV sprayers include payload capacity and battery life, sprayer system, endurance restrictions, weather, civil aviation authority regulations, and legal constraints. The Limitations of the payload and battery life of small UAVs are not a major barrier when using a UAV to spray a small patch or a small field. But the market share for UAV pesticide applications will favor such UAVs that provide greater lift and longer flight endurance and offer exceptionally durable and rugged designs for operations under extreme agricultural conditions for pesticide applications. It is important to note that the design and utilization of the sprayer and intelligent spray mix formulation design are crucial for improving chemical deposition, penetrability, and drift loss. UAV sprayer application technology must continue to optimize the operating methods and select aviation-specific reagents to improve sprayer performance and pesticide utilization rates. Commercialization of UAV spraying technology requires the collaborative and constructive participation of stakeholders from the government, research institutions, industry, and end users.
References
22nd Air Force. (2023). Aerial Spray Mission. Retrieved May 30, 2023, from 22nd Air Force website: https://www.22af.afrc.af.mil/About-Us/Fact-Sheets/Display/Article/784165/aerial-spray mission/https%3A%2F%2Fwww.22af.afrc.af.mil%2FAbout-Us%2FFact Sheets%2FDisplay%2FArticle%2F784165%2Faerial-spray-mission%2F
Abbie. (2018). DJI Agras MG1 review - Perfect drone for agricultural needs. Outstanding Drone. Retrieved from https://www.outstandingdrone.com/dji-agras-mg1/
Abd. Kharim, M. N., Wayayok, A., Mohamed Shariff, A. R., Abdullah, A. F., & Husin, E. M. (2019). Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation. Comput. Electron. Agric., 167, 105045. https://doi.org/10.1016/j.compag.2019.105045
AESA. (2022). Agencia Estatal de Seguridad Aérea-Ministerio de Fomento. Retrieved from https://www.seguridadaerea.gob.es/en
Afif, M. T., & Pratiwi, I. A. (2015). Analisis Perbandingan Baterai Lithium-Ion, Lithium-Polymer, Lead Acid dan Nickel-Metal Hydride pada Penggunaan Mobil Listrik - Review. Jurnal Rekayasa Mesin, 6(2). https://doi.org/10.21776/ub.jrm.2015.006.02.1
AG NAV. (2022). Flow control system for aerial applications. Retrieved from https://www.agnav.com/application-control/gps-auto-flow-control/
Agridrones Solutions Israel. (2017). File:4X-UHJ Agridrones d.jpg. Retrieved from https://commons.wikimedia.org/wiki/File:4XUHJ_Agridrones_d.jpg#metadat
Ahmad, F., Qiu, B., Dong, X., Ma, J., Huang, X., Ahmed, S., & Ali Chandio, F. (2020). Effect of operational parameters of UAV sprayer on spray deposition pattern in target and off-target zones during outer field weed control application. Comput. Electron. Agric., 172, 105350. https://doi.org/10.1016/j.compag.2020.105350
AliExpress. (2022). JMRRC Drone Store - Amazing prodcuts with exclusive discounts on AliExpress. AliExpress. Retrieved from https://jmrrc.aliexpress.com/store/5510069
Anand, K., & Goutam, R. (2019). An autonomous UAV for pesticide spraying. Int. J. Trend Sci. Res. Dev. - IJTSRD, 3(3), 986-990. https://doi.org/10.31142/ijtsrd23161
Angurala, M., Bala, M., & Bamber, S. S. (2022). Wireless battery recharging through UAV in wireless sensor networks. Egypt. Inform. J., 23(1), 21-31. https://doi.org/10.1016/j.eij.2021.05.002
Antonelli, J. (2016). 2016: Most section 333s just $1500. Drone Laws. Retrieved from https://dronelawsblog.com/2016-most-section-333s-just-1500/
Antuniassi, U. R. (2015). Evolution of agricultural aviation in Brazil. Outlooks Pest Manag., 26(1), 12-15. https://doi.org/10.1564/v26_feb_04
Atherton, K. (2016). This drone sprays pesticides around crops. Popular Science. Retrieved from https://www.popsci.com/agri-drone-is-precision-pesticide-machine/
ATP Flight School. (2022). How long to become a pilot? Retrieved from https://atpflightschool.com/become-a-pilot/airline-career/how-long-to-become-a-pilot.html
Australian Government, Civil Aviation Safety Authority. (2022). Know your drone. Retrieved from https://www.casa.gov.au/knowyourdrone
AUVSI NEWS. (2013). Are UAS more cost effective than manned flights? Association for Unmanned Vehicle Systems International. Retrieved from https://www.auvsi.org/are-uas-more-cost-effective-manned-flights
Bae, Y., & Mo Koo, Y. (2013). Flight attitudes and spray patterns of a roll-balanced agricultural unmanned helicopter. Appl. Eng. Agric., 29(5), 675-682. https://doi.org/10.13031/aea.29.10059
Beck, B., Brusselman, E., Nuyttens, D., Moens, M., Pollet, S., Temmerman, F., & Spanoghe, P. (2013). Improving foliar applications of entomopathogenic nematodes by selecting adjuvants and spray nozzles. Biocontrol Sci. Technol., 23(5), 507-520. https://doi.org/10.1080/09583157.2013.777692
Biglia, A., Grella, M., Bloise, N., Comba, L., Mozzanini, E., Sopegno, A.,... Gay, P. (2022). UAV-spray application in vineyards: Flight modes and spray system adjustment effects on canopy deposit, coverage, and off-target losses. Sci. Total Environ., 845, 157292. https://doi.org/10.1016/j.scitotenv.2022.157292
Bird, S. L., Esterly, D. M., & Perry, S. G. (1996). Off-target deposition of pesticides from agricultural aerial spray applications. J. Environ. Qual., 25(5), 1095-1104. https://doi.org/10.2134/jeq1996.00472425002500050024x
Böhler, J. E., Schaepman, M. E., & Kneubühler, M. (2018). Crop classification in a heterogeneous arable landscape using uncalibrated UAV data. Remote Sens., 10(8), 1282. https://doi.org/10.3390/rs10081282
Campos, J., Llop, J., Gallart, M., García-Ruiz, F., Gras, A., Salcedo, R., & Gil, E. (2019). Development of canopy vigour maps using UAV for site-specific management during vineyard spraying process. Precis. Agric., 20(6), 1136-1156. https://doi.org/10.1007/s11119-019-09643-z
Cavalaris, C., Karamoutis, C., & Markinos, A. (2022). Efficacy of cotton harvest aids applications with unmanned aerial vehicles (UAV) and ground-based field sprayers – A case study comparison. Smart Agric. Technol., 2, 100047. https://doi.org/10.1016/j.atech.2022.100047
CDC. (2004). Work-related pilot fatalities in agriculture - United States, 1992-2001. Centers for Disease Control and Prevention. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5315a4.htm
Chen, P., Lan, Y., Douzals, J.-P., Ouyang, F., Wang, J., & Xu, W. (2020a). Droplet distribution of Unmanned Aerial Vehicle under several spray volumes and canopy heights in the cotton canopy. Int. J. Precis. Agric. Aviat., 3(4). https://doi.org/10.33440/j.ijpaa.20200304.136
Chen, P., Ouyang, F., Wang, G., Qi, H., Xu, W., Yang, W.,... Lan, Y. (2021a). Droplet distributions in cotton harvest aid applications vary with the interactions among the unmanned aerial vehicle spraying parameters. Ind. Crops Prod., 163, 113324. https://doi.org/10.1016/j.indcrop.2021.113324
Chen, S., Lan, Y., Zhou, Z., Deng, X., & Wang, J. (2021b). Research advances of the drift reducing technologies in application of agricultural aviation spraying. Int. J. Agric. Biol. Eng., 14(5). https://doi.org/10.25165/j.ijabe.20211405.6225
Chen, S., Lan, Y., Zhou, Z., Ouyang, F., Wang, G., Huang, X.,... Cheng, S. (2020b). Effect of droplet size parameters on droplet deposition and drift of aerial spraying by using plant protection UAV. Agronomy, 10(2), 195. https://doi.org/10.3390/agronomy10020195
Cheng, Y., Xu, S., Huang, Z., Xiong, B., & Wang, C. (2014). Stability control of small fixed-wing UAV. Proc. 11th World Congress on Intelligent Control and Automation, (pp. 1056-1060). https://doi.org/10.1109/WCICA.2014.7052863
Chung, W., & Son, H. (2020). Fault-tolerant control of multirotor UAVs by control variable elimination. IEEE/ASME Trans. Mechatron., 25(5), 2513-2522. https://doi.org/10.1109/TMECH.2020.2982436
Ciobanu, E. (2022). How long does a drone battery last? What you need to know! Droneblog. Retrieved from https://www.droneblog.com/how-long-does-a-drone-battery-last-what-you-need-to-know/
Core, J. (2005). Agricultural aircraft offer a different view of remote sensing. AgResearch Mag. Retrieved from http://www.ars.usda.gov/is/AR/archive/mar05/remote0305.htm
Coxworth, B. (2020). Autonomous electric crop duster gets approval for US demos. New Atlas. Retrieved from https://newatlas.com/drones/pyka-pelican-autonomous-electric-crop-spraying-drone/
Cunha, M., Carvalho, C., & Marcal, A. R. (2012). Assessing the ability of image processing software to analyse spray quality on water-sensitive papers used as artificial targets. Biosyst. Eng., 111(1), 11-23. https://doi.org/10.1016/j.biosystemseng.2011.10.002
Delavarpour, N., Koparan, C., Nowatzki, J., Bajwa, S., & Sun, X. (2021). A technical study on UAV characteristics for precision agriculture applications and associated practical challenges. Remote Sens., 13(6), 1204. https://doi.org/10.3390/rs13061204
DJI. (2022a). Agras T16-DJI. Retrieved from https://www.dji.com/t16?site=brandsite&from=nav
DJI. (2022b). AGRAS T20-DJI. Retrieved from https://www.dji.com/t20
DJI. (2022c). MG-1 specs. Retrieved from https://www.dji.com/mg-1/info#specs
DroneBlogger. (2021). XAG releases set of agric drones onto the world market. Drones.R.Africa. Retrieved from https://dronenews.africa/xag-releases-drones-to-the-world/
ECFR. (2022a). 14 CFR 107.36 - Carriage of hazardous material. Electronic Code of Federal Regulations. Retrieved from https://www.ecfr.gov/current/title-14/chapter-I/subchapter-F/part-107/subpart-B/section-107.36
ECFR. (2022b). 14 CFR Part 107-Small unmanned aircraft systems. Electronic Code of Federal Regulations. Retrieved from https://www.ecfr.gov/current/title-14/chapter-I/subchapter-F/part-107
ECFR. (2022c). 14 CFR 91.119 - Minimum safe altitudes: General. Electronic Code of Federal Regulations. Retrieved from https://www.ecfr.gov/current/title-14/chapter-I/subchapter-F/part-91/subpart-B/subject-group-ECFRe4c59b5f5506932/section-91.119
Ehsan, S., & McDonald-Maier, K. D. (2009). On-board vision processing for small UAVs: Time to rethink strategy. Proc. 2009 NASA/ESA Conf. on Adaptive Hardware and Systems (pp. 75-81). IEEE. https://doi.org/10.1109/AHS.2009.6
Elsik, C. M., Stridde, H. M., & Schweiner, T. M. (2010). Spray drift reduction technology adjuvant evaluation. J. ASTM Int. (JAI), 7(7). https://doi.org/10.1520/JAI102902
Enz, J., Hofman, V., & Thostenson, A. (2019). Air temperature inversions causes, characteristics and potential effects on pesticide spray drift. AE1705. NDSU Extension. Retrieved from https://www.ag.ndsu.edu/publications/crops/air-temperature-inversions-causes-characteristics-and-potential-effects-on-pesticide-spray-drift
EPA. (2013a). Pesticide registraction - Labeling requirements. Retrieved from https://www.epa.gov/pesticide-registration/labeling-requirements
EPA. (2013b). Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and federal facilities. Retrieved from https://www.epa.gov/enforcement/federal-insecticide-fungicide-and-rodenticide-act-fifra-and-federal-facilities
EPA. (2014). Introduction to pesticide drift. Retrieved from https://www.epa.gov/reducing-pesticide-drift/introduction-pesticide-drift
FAA. (2019). 5 facts about manned aerial application. Farm Progress. Retrieved from https://www.farmprogress.com/herbicide/5-facts-about-manned-aerial-application
FAA. (2022a). Advisory circular, 137-1B. U.S. Department of Transportation, Federal Aviation Administration. Retrieved from https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_137-1B.pdf
FAA. (2022b). II.4.3_Aerial_Application_Operations.doc. Federal Aviation Administration. Retrieved from https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fwww.faa.gov%2Fsites%2Ffaa.gov%2Ffiles%2Fabout%2Foffice_org%2Fheadquarters_offices%2Favs%2FII.4.3_Aerial_Application_Operations.doc&wdOrigin=BROWSELINK
FAA. (2022c). Part 107 waiver. Federal Aviation Administration. Retrieved from https://www.faa.gov/uas/commercial_operators/part_107_waivers/
FAA. (2022d). Flying near airports. Federal Aviation Administration. Retrieved from https://www.faa.gov/uas/getting_started/where_can_i_fly/airspace_restrictions/flying_near_airports
FAA. (2022e). Part 107 airspace authorizations. Federal Aviation Administration. Retrieved from https://www.faa.gov/uas/commercial_operators/part_107_airspace_authorizations
FAA. (2022f). Advisory circular, 137-1A. U.S. Department of Transportation, Federal Aviation Administration. Retrieved from https://www.agaviation.org/Files/Careers/AC137-1A_Cert_of_%20Ag_Operators.pdf
FAA. (2022g). Certificates of Waiver or Authorization (COA). Federal Aviation Administration. Retrieved from https://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/systemops/aaim/organizations/uas/coa
FAA. (2022h). Airspace 101 - Rules of the sky. Federal Aviation Administration. Retrieved from https://www.faa.gov/uas/getting_started/where_can_i_fly/airspace_101
Faiçal, B. S., Costa, F. G., Pessin, G., Ueyama, J., Freitas, H., Colombo, A.,... Braun, T. (2014b). The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides. J. Syst. Archit., 60(4), 393-404. https://doi.org/10.1016/j.sysarc.2014.01.004
Faiçal, B. S., Pessin, G., Filho, G. P., Carvalho, A. C., Furquim, G., & Ueyama, J. (2014a). Fine-tuning of UAV control rules for spraying pesticides on crop fields. Proc. 2014 IEEE 26th Int. Conf. on Tools with Artificial Intelligence, (pp. 527-533). https://doi.org/10.1109/ICTAI.2014.85
FAS. (1999). General Atomic GNAT-75. FAS, Intelligence Resource Program. Retrieved from https://irp.fas.org/program/collect/gnat-750.htm
Federal Register. (2006). Hazardous materials: Revision of requirements for carriage by aircraft. Federal Register. Retrieved from https://www.federalregister.gov/documents/2006/03/22/06-2596/hazardous-materials-revision-of-requirements-for-carriage-by-aircraft
Ferguson, J. C., Chechetto, R. G., Adkins, S. W., Hewitt, A. J., Chauhan, B. S., Kruger, G. R., & O’Donnell, C. C. (2018). Effect of spray droplet size on herbicide efficacy on four winter annual grasses. Crop Prot., 112, 118-124. https://doi.org/10.1016/j.cropro.2018.05.020
Flydragon. (2019). Agriculture drone sprayer system. Fly Dragon Drone Tech. Retrieved from http://www.dronefromchina.com/academy/agriculture-drone-sprayer-system.html
Frankelius, P., Norrman, C., & Johansen, K. (2019). Agricultural innovation and the role of institutions: Lessons from the game of drones. J. Agric. Environ. Ethics, 32(5), 681-707. https://doi.org/10.1007/s10806-017-9703-6
Freeman, P. K., & Freeland, R. S. (2014). Politics & technology: U.S. polices restricting unmanned aerial systems in agriculture. Food Policy, 49, 302-311. https://doi.org/10.1016/j.foodpol.2014.09.008
Fritz, B. (2019). Best practices for aerial application - Webinar materials. United States Environmental Protection Agency. Retrieved from https://www.epa.gov/reducing-pesticide-drift/best-practices-aerial-application-webinar-materials
Fritz, B. K. (2006). Meteorological effects on deposition and drift of aerially applied spray. Trans. ASABE, 49(5), 1295-1301. Retrieved from https://doi.org/10113/1458
Fritz, B. K., & Hoffmann, W. C. (2008). Atmospheric effects on fate of aerially applied agricultural sprays. Agric. Eng. Int.: CIGR J., 10. Retrieved from https://cigrjournal.org/index.php/Ejounral/article/view/1241
Fritz, B. K., Hoffmann, W. C., Lan, Y., Thomson, S. J., & Huang, Y. (2008). Low-level atmospheric temperature inversions and atmospheric stability: Characteristics and impacts on agricultural applications. Agric. Eng. Int.: CIGR J., 10.
Fritz, B., Hoffmann, C., Bagley, W., & Hewitt, A. (2011). Field scale evaluation of spray drift reduction technologies from ground and aerial application systems. J. ASTM Int. (JAI), 8(5). https://doi.org/10.1520/JAI103457
Gallagher, S. (2015). Crop-dusting unmanned helicopter gets cleared for commercial flight. Ars Technica. Retrieved from https://arstechnica.com/information-technology/2015/05/crop-dusting-unmanned-helicopter-gets-cleared-for-commercial-flight/
Gao, Q., Ma, J., Liu, Q., Liao, M., Xiao, J., Jiang, M.,... Cao, H. (2020). Effect of application method and formulation on prothioconazole residue behavior and mycotoxin contamination in wheat. Sci. Total Environ., 729, 139019. https://doi.org/10.1016/j.scitotenv.2020.139019
Garg, P. K. (2021). Unmanned aerial vehicles: An introduction. Mercury Learning and Information.
Gil, E., Balsari, P., Gallart, M., Llorens, J., Marucco, P., Andersen, P. G.,... Llop, J. (2014). Determination of drift potential of different flat fan nozzles on a boom sprayer using a test bench. Crop Prot., 56, 58-68. https://doi.org/10.1016/j.cropro.2013.10.018
Giles, D. K. (2016). Use of remotely piloted aircraft for pesticide applications: Issues and outlook. Outlooks Pest Manag., 27(5), 213-216. https://doi.org/10.1564/v27_oct_05
Giles, D. K., Billing, R., & Singh, W. (2018). Remotely piloted aircraft for agricultural spraying: Conclusions from multiple season operations. Aspects Appl. Biol., 137, 1-8. https://doi.org/10.1520/STP161920180082
Gomez, K., Rasheed, T., Reynaud, L., & Kandeepan, S. (2013). On the performance of aerial LTE base-stations for public safety and emergency recovery. 2013 IEEE Globecom Workshops (GC Wkshps), (pp. 1391-1396). https://doi.org/10.1109/GLOCOMW.2013.6825189
Grassi, M. (2019). Three agricultural spray drone models that promise to be breakthroughs (Part one). PrecisionAg. Retrieved from https://www.precisionag.com/in-field-technologies/drones-uavs/three-agricultural-spray-drone-models-that-promise-to-be-breakthroughs-part-one/
Guo, Q., Zhu, Y., Tang, Y., Hou, C., He, Y., Zhuang, J.,... Luo, S. (2020). CFD simulation and experimental verification of the spatial and temporal distributions of the downwash airflow of a quad-rotor agricultural UAV in hover. Comput. Electron. Agric., 172, 105343. https://doi.org/10.1016/j.compag.2020.105343
Guo, S., Li, J., Yao, W., Zhan, Y., Li, Y., & Shi, Y. (2019). Distribution characteristics on droplet deposition of wind field vortex formed by multi-rotor UAV. PLoS One, 14(7), e0220024. https://doi.org/10.1371/journal.pone.0220024
Hanson, B. (2008). Investigation of a non-uniform helicopter rotor downwash model. Thesis. Linköping University, Department of Electrical Engineering. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17193
Hao, Z., Li, M., Yang, W., & Li, X. (2022). Evaluation of UAV spraying quality based on 1D-CNN model and wireless multi-sensors system. Inf. Process. Agric. https://doi.org/10.1016/j.inpa.2022.07.004
Harirchi, P., & Yang, M. (2022). Exploration of Carbon Dioxide Curing of Low Reactive Alkali-Activated Fly Ash. Materials, 15(9), Article 9. https://doi.org/10.3390/ma15093357
Hassandokht Mashhadi, A., Handy, R., Farhadmanesh, M., Rashidi, A., Honda, T., Sleeth, D. K., & Henry, T. (2022). Feasibility study of using nebulizer-retrofitted UAVs at construction projects: The case study of residential jobsites in Utah. J. Constr. Eng. Manag., 148(10), 05022009. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002368
Hobson, P. A., Miller, P. C., Walklate, P. J., Tuck, C. R., & Western, N. M. (1993). Spray drift from hydraulic spray nozzles: The use of a computer simulation model to examine factors influencing drift. J. Agric. Eng. Res., 54(4), 293-305. https://doi.org/10.1006/jaer.1993.1022
Hock, W. (2022). Spray adjuvants. PennState Extension. Retrieved from https://extension.psu.edu/spray-adjuvants
Hofman, V., & Solseng, E. (2017). Reducing spray drift. AE1210. NDSU Extension Service. Retrieved from https://www.ag.ndsu.edu/publications/crops/reducing-spray-drift
Hossein Motlagh, N., Taleb, T., & Arouk, O. (2016). Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives. IEEE IoT J., 3(6), 899-922. https://doi.org/10.1109/JIOT.2016.2612119
HSE-UAV. (2022). LARGE CAPACITY SPRAYING DRONE | M8A PRO (AG-V8A+) | HSEUAV. Retrieved from https://hse-uav.com/product/m8a-pro-spraying-drone/
Huang, Y. (2019). Perspectives and experiences on the development and innovation of agricultural aviation and precision agriculture from the Mississippi Delta and recommendations for China. Smart Agric., 1(4), 12-30.
Huang, Y., & Fisher, D. K. (2019). A web guide system for aerial application to avoid off-target drift caused by temperature inversion through open-source weather stations. ASABE Paper No. 1900193. Proc. 2019 ASABE Annu. Int. Meeting. ASABE. https://doi.org/10.13031/aim.201900193
Huang, Y., & Thomson, S. J. (2016). Atmospheric stability evaluation at different time intervals for determination of aerial spray application timing. J. Biosyst. Eng., 41(4), 337-341. https://doi.org/10.5307/JBE.2016.41.4.337
Huang, Y., Ding, W., Thomson, S. J., Reddy, K. N., & Zablotowicz, R. M. (2012). Assessing crop injury caused by aerially applied glyphosate drift using spray sampling. Trans. ASABE, 55(3), 725-731. https://doi.org/10.13031/2013.41504
Huang, Y., Hoffmann, W. C., Lan, Y., Wu, W., & Fritz, B. K. (2009). Development of a spray system for an unmanned aerial vehicle platform. Appl. Eng. Agric., 25(6), 803-809. https://doi.org/10.13031/2013.29229
Huang, Y., Thomson, S. J., Hoffmann, W. C., Lan, Y., & Fritz, B. K. (2013). Development and prospect of unmanned aerial vehicle technologies for agricultural production management. Int. J. Agric. Biol. Eng., 6(3). https://doi.org/10.25165/ijabe.v6i3.900
Hunter III, J. E., Gannon, T. W., Richardson, R. J., Yelverton, F. H., & Leon, R. G. (2020). Integration of remote-weed mapping and an autonomous spraying unmanned aerial vehicle for site-specific weed management. Pest Manag. Sci., 76(4), 1386-1392. https://doi.org/10.1002/ps.5651
Hussain, M., Wang, Z., Huang, G., Mo, Y., Guo, Y., Kaousar, R.,... Tan, W. (2022). Evaluation of droplet deposition and efficiency of 28-homobrassinolide sprayed with unmanned aerial spraying system and electric air-pressure knapsack sprayer over wheat field. Comput. Electron. Agric., 202, 107353. https://doi.org/10.1016/j.compag.2022.107353
Hylio. (2022). Hylio’s largest agricultural spray drone | AG-122. Retrieved from https://www.hyl.io/ag-272-product-page
Isop, W. A., Gebhardt, C., Nägeli, T., Fraundorfer, F., Hilliges, O., & Schmalstieg, D. (2019). High-level teleoperation system for aerial exploration of indoor environments. Front. Robot. AI, 6. https://doi.org/10.3389/frobt.2019.00095
Kangunde, V., Jamisola, R. S., & Theophilus, E. K. (2021). A review on drones controlled in real-time. Int. J. Dyn. Control, 9(4), 1832-1846. https://doi.org/10.1007/s40435-020-00737-5
Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., & Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sens. Environ., 227, 61-73. https://doi.org/10.1016/j.rse.2019.03.025
Khofiyah, N. A., Sutopo, W., & Nugroho, B. D. (2019). Technical feasibility battery lithium to support unmanned aerial vehicle (UAV): A technical review. Proc. Int. Conf. on Industrial Engineering and Operations Management, 2019, pp. 3591-3601.
Kray Technologies. (2022). Drone build to replace AG planes. Retrieved from https://kray.technology/
Li, X., Andaloro, J. T., Lang, E. B., & Pan, Y. (2019). Best management practices for Unmanned Aerial Vehicles (UAVs) application of insecticide products on rice. ASABE Paper No. 1901493. Proc. 2019 ASABE Annual Int. Meeting. ASABE. https://doi.org/10.13031/aim.201901493
Li, X., Giles, D. K., Andaloro, J. T., Long, R., Lang, E. B., Watson, L. J., & Qandah, I. (2021). Comparison of UAV and fixed-wing aerial application for alfalfa insect pest control: Evaluating efficacy, residues, and spray quality. Pest Manag. Sci., 77(11), 4980-4992. https://doi.org/10.1002/ps.6540
Li, Y., Xu, Y., Xue, X., Liu, X., & Liu, X. (2022). Optimal spraying task assignment problem in crop protection with multi-UAV systems and its order irrelevant enumeration solution. Biosyst. Eng., 214, 177-192. https://doi.org/10.1016/j.biosystemseng.2021.12.018
Liao, J., Zang, Y., Luo, X., Zhou, Z., Zang, Y., Wang, P., & Hewitt, A. J. (2020). The relations of leaf area index with the spray quality and efficacy of cotton defoliant spraying using unmanned aerial systems (UASs). Comput. Electron. Agric., 169, 105228. https://doi.org/10.1016/j.compag.2020.105228
Liu, Y., Huang, X., Zhang, Y., & Zhou, Y. (2018). Dynamic stability and control of a manipulating unmanned aerial vehicle. Int. J. Aerospace Eng., 2018, e3481328. https://doi.org/10.1155/2018/3481328
Lou, Z., Xin, F., Han, X., Lan, Y., Duan, T., & Fu, W. (2018). Effect of unmanned aerial vehicle flight height on droplet distribution, drift and control of cotton aphids and spider mites. Agronomy, 8(9), 187. https://doi.org/10.3390/agronomy8090187
Malewar, A. (2019). Volocopter designed a giant agricultural drone sprayer with John Deere. Inceptive Mind. Retrieved from https://www.inceptivemind.com/volocopter-designed-giant-agricultural-drone-sprayer-john-deere/10137/
Martin, D. E., Woldt, W. E., & Latheef, M. A. (2019). Effect of application height and ground speed on spray pattern and droplet spectra from remotely piloted aerial application systems. Drones, 3(4), 83. https://doi.org/10.3390/drones3040083
Martinez-Guanter, J., Agüera, P., Agüera, J., & Pérez-Ruiz, M. (2020). Spray and economics assessment of a UAV-based ultra-low-volume application in olive and citrus orchards. Precis. Agric., 21(1), 226-243. https://doi.org/10.1007/s11119-019-09665-7
Mazur, M. (2016). Six ways drones are revolutionizing agriculture. E-Agriculture, FAO. Retrieved from https://www.fao.org/e-agriculture/news/six-ways-drones-are-revolutionizing-agriculture
McNeil, B. E., Pisek, J., Lepisk, H., & Flamenco, E. A. (2016). Measuring leaf angle distribution in broadleaf canopies using UAVs. Agric. For. Meteorol., 218-219, 204-208. https://doi.org/10.1016/j.agrformet.2015.12.058
Meng, Y. H., Han, Y. X., Liang, Z. J., Su, J. Y., & Lan, Y. B. (2020a). Harvest-aid application strategy in different cotton planting densities by unmanned aerial vehicle: Effects of dosage and application frequency on defoliation efficacy, boll opening rate, fiber quality, and lint cotton yield. Int. J. Precis. Agric. Aviat., 2(1).
Meng, Y., Lan, Y., Mei, G., Guo, Y., Song, J., & Wang, Z. (2018). Effect of aerial spray adjuvant applying on the efficiency of small unmanned aerial vehicle for wheat aphids control. Int. J. Agric. Biol. Eng., 11(5), 46-53.
Meng, Y., Song, J., Lan, Y., Mei, G., Liang, Z., & Han, Y. (2019). Harvest aids efficacy applied by unmanned aerial vehicles on cotton crop. Ind. Crops Prod., 140, 111645. https://doi.org/10.1016/j.indcrop.2019.111645
Meng, Y., Su, J., Song, J., Chen, W.-H., & Lan, Y. (2020b). Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution. Comput. Electron. Agric., 170, 105282. https://doi.org/10.1016/j.compag.2020.105282
Mohidem, N. A., Che’Ya, N. N., Juraimi, A. S., Fazlil Ilahi, W. F., Mohd Roslim, M. H., Sulaiman, N.,... Mohd Noor, N. (2021). How can unmanned aerial vehicles be used for detecting weeds in agricultural fields? Agriculture, 11(10), 1004.
Moore, A. (2019). A realistic look at UAVs and aerial application in agriculture. PrecisionAg. Retrieved from https://www.precisionag.com/in-field-technologies/drones-uavs/a-realistic-look-at-uavs-and-aerial-application-in-agriculture/
Mukherjee, A., Misra, S., Sukrutha, A., & Raghuwanshi, N. S. (2020). Distributed aerial processing for IoT-based edge UAV swarms in smart farming. Comput. Networks, 167, 107038. https://doi.org/10.1016/j.comnet.2019.107038
NAAA. (2022a). 5 facts about manned aerial application. The National Agricultural Aviation Association. Retrieved from https://www.farmprogress.com/herbicide/5-facts-about-manned-aerial-application
NAAA. (2022b). Industry facts. National Agricultural Aviation Association. Retrieved from https://www.agaviation.org/industryfacts
NAAA. (2022c). Five facts you need to know about aerial application and UAVs. National Agricultural Aviation Association. Retrieved from https://www.agaviation.org/uav-aerial-application-facts
NAAA. (2022d). The search for the next generation of AG pilots is on! Retrieved from https://www.agaviation.org/Files/NAAA-Agricultural-Aviation-Career-Poster.pdf
NAAA. (2022e). USDA-ARS Aerial Spray Nozzle Models. Retrieved from https://www.agaviation.org/spraynozzlemodels
Nowatzki, J. (2009). Choose the right nozzles for field sprayers. NDSU Agriculture Communication. Retrieved from https://www.ag.ndsu.edu/news/newsreleases/2009/june-15-2009/choose-the-right-nozzles-for-field-sprayers
Nozawa, T. (2022). New battery technologies still years away. Nikkei Asia. Retrieved from https://asia.nikkei.com/Business/Technology/New-battery-technologies-still-years-away
NTSB. (2022). Accident Synopses - by month. National Transportation Safety Board. Retrieved from https://www.ntsb.gov/Pages/monthly.aspx
OM UVA Systems. (2022). Kisan crop duster is a fully autonomous. Retrieved from http://www.omuavsystems.com/Kisan-Crop-Duster.html
Ozkan, E. (2016). New nozzles for spray drift reduction. Ohioline - Ohio State University Extension. Retrieved from https://ohioline.osu.edu/factsheet/fabe-523
Peters, T., Thostenson, A., Nowatzki, J., Hofman, V., & Wilson, J. (2017). Selecting spray nozzles to reduce particle drift-publications. AE1246. NDSU Extension Service. Retrieved from https://www.ag.ndsu.edu/publications/crops/selecting-spray-nozzles-to-reduce-particle-drift
Petty, R. V. (2018). Drone use in aerial pesticide application faces outdated regulatory hurdles. Harvard J. Law Technol. Digest. Retrieved from https://jolt.law.harvard.edu/assets/digestImages/Digest_Petty_Ryan_Note_Fall17_Final.pdf
PR Newswire. (2020). DJI’s latest AGRAS T20 drone makes agricultural spraying easier, smarter and safer. sUAS News. Retrieved from https://www.suasnews.com/2020/11/djis-latest-agras-t20-drone-makes-agricultural-spraying-easier-smarter-and-safer/
Price, R. (2018). Nozzle tip selection for drift control. LSU AgCenter. Retrieved from https://www.lsuagcenter.com/articles/page1535055262311
Price, R., Lee, D., & Harrell, D. (2019). Nozzle tip and sprayer setting selection for drift reduction in the DJI AGRAS MG-1/1S sprayer drone. LSU AgCenter. Retrieved from https://www.lsuagcenter.com/articles/page1573085106771
Qi, H., Lin, Z., Zhou, J., Li, J., Chen, P., & Ouyang, F. (2020). Effect of temperature and humidity on droplet deposition of unmanned agricultural aircraft system. Int. J. Precis. Agric. Aviat., 3(3), 41-49. https://doi.org/10.33440/j.ijpaa.20200303.100
Qin, W., Xue, X., Zhang, S., Gu, W., & Wang, B. (2018). Droplet deposition and efficiency of fungicides sprayed with small UAV against wheat powdery mildew. Int. J. Agric. Biol. Eng., 11(2). https://doi.org/10.25165/ijabe.v11i2.3157
Qin, W.-C., Qiu, B.-J., Xue, X.-Y., Chen, C., Xu, Z.-F., & Zhou, Q.-Q. (2016). Droplet deposition and control effect of insecticides sprayed with an unmanned aerial vehicle against plant hoppers. Crop Prot., 85, 79-88. https://doi.org/10.1016/j.cropro.2016.03.018
Quan, G., Nong, Y., Ji, S., & Tui, G. (2015). Plant protection and pesticide application techniques Application Guide (Chinese Edition). China Agriculture Press.
Rabah, M., Rohan, A., Talha, M., Nam, K.-H., & Kim, S. H. (2018). Autonomous vision-based target detection and safe landing for UAV. Int. J. Control Autom. Syst., 16(6), 3013-3025. https://doi.org/10.1007/s12555-018-0017-x
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Comput. Networks, 172, 107148. https://doi.org/10.1016/j.comnet.2020.107148
Rahman, M. A., Wang, X., & Wen, C. (2014). A review of high energy density lithium-air battery technology. J. Appl. Electrochem., 44(1), 5-22. https://doi.org/10.1007/s10800-013-0620-8
Rau, B. (2021). Drift, drones and aerial pesticide label decisions. Agric. Aviat. Retrieved from https://www.agaviationmagazine.org/agriculturalaviation/winter_2021/MobilePagedArticle.action?articleId=1655311
RDRONGUY TECHNOLOGY. (2022). EFT E616P E616S 16KG Agricultural Spraying Drone 16L and JIYI K++ Flight Control Hobbywing X8 Motor Custom Drone. Retrieved from https://www.cropsprayuav.com/product/5072646667663267.html
Richardson, B., Rolando, C. A., & Kimberley, M. O. (2020). Quantifying spray deposition from a UAV configured for spot spray applications to individual plants. Trans. ASABE, 63(4), 1049-1058. https://doi.org/10.13031/trans.13724
Ridwan, I., & Alfindo. (2019). The effect of use of solar panels on micro scale fixed-wing UAV type as a power recharging system. AIP Conf. Proc., 2180(1). https://doi.org/10.1063/1.5135553
Satloc. (2022). Satloc G4: High quality complete system. Retrieved from https://satloc.com/products/satloc-g4/
Service, R. F. (2018). Lithium-sulfur batteries poised for leap. Science, 359(6380), 1080-1081. https://doi.org/10.1126/science.359.6380.1080
Smith, D. B. (1992). Uniformity and recovery of broadcast sprays using fan nozzles. Trans. ASAE, 35(1), 39-44. https://doi.org/10.13031/2013.28567
Song, C., Zhou, Z., Zang, Y., Zhao, L., Yang, W., Luo, X.,... Zhu, Q. (2021). Variable-rate control system for UAV-based granular fertilizer spreader. Comput. Electron. Agric., 180, 105832. https://doi.org/10.1016/j.compag.2020.105832
Stefas, N., Bayram, H., & Isler, V. (2019). Vision-based monitoring of orchards with UAVs. Comput. Electron. Agric., 163, 104814. https://doi.org/10.1016/j.compag.2019.05.023
Sumner, P. E. (2012). Sprayer Nozzle Selection. Bulletin 1158. The University of Georgia Cooperative Extension. Retrieved from https://extension.uga.edu/publications/detail.html?number=B1158
Tactical Robotics LTD. (2022). Cormorant - Tactical robotics. Retrieved from https://www.tactical-robotics.com/category/cormorant
Tang, Y., Hou, C. J., Luo, S. M., Lin, J. T., Yang, Z., & Huang, W. F. (2018). Effects of operation height and tree shape on droplet deposition in citrus trees using an unmanned aerial vehicle. Comput. Electron. Agric., 148, 1-7. https://doi.org/10.1016/j.compag.2018.02.026
Teske, A. L., Chen, G., Nansen, C., & Kong, Z. (2019). Optimised dispensing of predatory mites by multirotor UAVs in wind: A distribution pattern modelling approach for precision pest management. Biosyst. Eng., 187, 226-238. https://doi.org/10.1016/j.biosystemseng.2019.09.009
Teske, M. E., Wachspress, D. A., & Thistle, H. W. (2018). Prediction of aerial spray release from UAVs. Trans. ASABE, 61(3), 909-918. https://doi.org/10.13031/trans.12701
THANOS. (2020). SYENA - Q10 Sprayer drone by Thanos. Thanos Technologies. Retrieved from http://www.thanos.in/products/thanos-agri-drone-syena-q10-product-brochure-10-01-2020
Thompson, R. M. (2012). Drones in domestic surveillance operations: Fourth amendment implications and legislative responses. University of North Texas Libraries, UNT Digital Library, Congressional Research Service. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc462113/
Thomson, S. J., Huang, Y., & Fritz, B. K. (2017). Atmospheric stability intervals influencing the potential for off-target movement of spray in aerial application. Int. J. Agric. Sci. Technol., 5(1), 1-17. https://doi.org/10.12783/ijast.2017.0501.01
Tian, Z., Xue, X., Cui, L., Chen, C., & Peng, B. (2020). Droplet deposition characteristics of plant protection UAV spraying at night. Int. J. Precis. Agric. Aviat., 3(4). https://doi.org/10.33440/j.ijpaa.20200304.103
Tippetts, B. (2008). Real-time implementation of vision algorithm for control, stabilization, and target tracking for a hovering micro-UAV. MS thesis. BYU ScholarsArchive. Retrieved from https://scholarsarchive.byu.edu/etd/1418
Torrent, X., Gregorio, E., Douzals, J.-P., Tinet, C., Rosell-Polo, J. R., & Planas, S. (2019). Assessment of spray drift potential reduction for hollow-cone nozzles: Part 1. Classification using indirect methods. Sci. Total Environ., 692, 1322-1333. https://doi.org/10.1016/j.scitotenv.2019.06.121
Tumbagi, K. K., & Pradyumna, N. (2021). Agro-based RC aircraft. In A. Chakrabarti, R. Poovaiah, P. Bokil, & V. Kant (Eds.), Smart innovation, systems and technologies: Design for tomorrow - Volume 3 - Proc. ICORD (pp. 425-438). Singapore: Springer. https://doi.org/10.1007/978-981-16-0084-5_34
Vanderhorst, H. R., Suresh, S., Renukappa, S., & Heesom, D. (2021). Strategic framework of Unmanned Aerial Systems integration in the disaster management public organisations of the Dominican Republic. Int. J. Disaster Risk Reduct., 56, 102088. https://doi.org/10.1016/j.ijdrr.2021.102088
Veille, V. (2017). Hercules 20, drone pulvérisateur de précision. mon-ViTi. Retrieved from https://www.mon-viti.com/filinfo/viticulture/hercules-20-drone-pulverisateur-de-precision
Wang, C., He, X., Wang, X., Wang, Z., Wang, S., Li, L.,... Wang, Z. (2018a). Testing method and distribution characteristics of spatial pesticide spraying deposition quality balance for unmanned aerial vehicle. Int. J. Agric. Biol. Eng., 11(2), 18–26. https://doi.org/10.25165/j.ijabe.20181102.3187
Wang, C., Herbst, A., Zeng, A., Wongsuk, S., Qiao, B., Qi, P.,... He, X. (2021a). Assessment of spray deposition, drift and mass balance from unmanned aerial vehicle sprayer using an artificial vineyard. Sci. Total Environ., 777, 146181. https://doi.org/10.1016/j.scitotenv.2021.146181
Wang, C., Zeng, A., He, X., Song, J., Andreas, H., & Gao, W. (2020a). Spray drift characteristics test of unmanned aerial vehicle spray unit under wind tunnel conditions. Int. J. Agric. Biol. Eng., 13(3). https://doi.org/10.25165/ijabe.v13i3.5716
Wang, G., Han, Y., Li, X., Andaloro, J., Chen, P., Hoffmann, W. C.,... Lan, Y. (2020b). Field evaluation of spray drift and environmental impact using an agricultural unmanned aerial vehicle (UAV) sprayer. Sci. Total Environ., 737, 139793. https://doi.org/10.1016/j.scitotenv.2020.139793
Wang, G., Lan, Y., Qi, H., Chen, P., Hewitt, A., & Han, Y. (2019). Field evaluation of an unmanned aerial vehicle (UAV) sprayer: Effect of spray volume on deposition and the control of pests and disease in wheat. Pest Manag. Sci., 75(6), 1546-1555. https://doi.org/10.1002/ps.5321
Wang, J., Lan, Y. B., Zhang, H., Zhang, Y. L., Wen, S., Yao, W., & Deng, J. (2018b). Drift and deposition of pesticide applied by UAV on pineapple plants under different meteorological conditions. Int. J. Agric. Biol. Eng., 11, 5-12. https://doi.org/10.25165/j.ijabe.20181106.4038
Wang, L., Xu, M., Hou, Q., Wang, Z., Lan, Y., & Wang, S. (2021b). Numerical verification on influence of multi-feature parameters to the downwash airflow field and operation effect of a six-rotor agricultural UAV in flight. Comput. Electron. Agric., 190, 106425. https://doi.org/10.1016/j.compag.2021.106425
Wen, S., Zhang, Q., Yin, X., Lan, Y., Zhang, J., & Ge, Y. (2019). Design of plant protection UAV variable spray system based on neural networks. Sensors, 19(5), 1112. https://doi.org/10.3390/s19051112
Willis, G. H., & McDowell, L. L. (1987). Pesticide persistence on foliage. In G. W. Ware (Ed.), Reviews of environmental contamination and toxicology: Continuation of residue reviews (Vol. 100, pp. 23-73). New York, NY: Springer. https://doi.org/10.1007/978-1-4612-4804-0_2
Wolf, R. E., Gardisser, D. R., & Minihan, C. L. (2003). Field comparisons for drift reducing/deposition aid tank mixes. ASAE Paper No: AA03-002. Retrieved from https://www.garrco.com/assets/website-2-controldriftresearch.pdf
XAG. (2022). XAG P40 agricultural drone. Retrieved from https://www.xa.com/en/p40
Xin, F., Zhao, J., Zhou, Y., Wang, G., Han, X., Fu, W.,... Lan, Y. (2018). Effects of dosage and spraying volume on cotton defoliants efficacy: A case study based on application of unmanned aerial vehicles. Agronomy, 8(6), 85. https://doi.org/10.3390/agronomy8060085
Xiongkui, H., Bonds, J., Herbst, A., & Langenakens, J. (2017). Recent development of unmanned aerial vehicle for plant protection in East Asia. Int. J. Agric. Biol. Eng., 10(3), 18-30.
Xue, X., Lan, Y., Sun, Z., Chang, C., & Hoffmann, W. C. (2016). Develop an unmanned aerial vehicle based automatic aerial spraying system. Comput. Electron. Agric., 128, 58-66. https://doi.org/10.1016/j.compag.2016.07.022
Yamaha. (2022a). Precision agriculture - Fazer R. Retrieved from https://www.yamahamotorsports.com/motorsports/pages/precision-agriculture-fazer-r
Yamaha. (2022b). Precision Agriculture - RMAX. Retrieved from https://www.yamahamotorsports.com/motorsports/pages/precision-agriculture-rmax
Yang, B., He, Y., Han, J., & Liu, G. (2014). Modeling and control of rotor-flying multi-joint manipulator. IFAC Proc. Vol., 47(3), 11024-11029. https://doi.org/10.3182/20140824-6-ZA-1003.00400
Yang, X., & Pei, X. (2022). 15 - Hybrid system for powering unmanned aerial vehicles: Demonstration and study cases. In M. Lo Faro, O. Barbera, & G. Giacoppo (Eds.), Hybrid Technologies for Power Generation (pp. 439-473). Academic Press. https://doi.org/10.1016/B978-0-12-823793-9.00014-0
Yao, W., Lan, Y., Wang, J., Wen, S., Wang, G., & Chen, S. (2017). Droplet drift characteristics of aerial spraying of AS350B3e helicopter. Trans. CSAE, 33, 75-83. https://doi.org/10.11975/j.issn.1002-6819.2017.22.010
Yinka-Banjo, C., & Ajayi, O. (2019). Sky-farmers: Applications of unmanned aerial vehicles (UAV) in agriculture. In G. Dekoulis (Ed.), Autonomous vehicles (pp. 107-128). https://doi.org/10.5772/intechopen.89488
Yu, K., Liu, Y., Gong, Z., Liang, Y., Du, L., Zhang, Z.,... Li, Z. (2022). Chemical topping improves the efficiency of spraying harvest aids using unmanned aerial vehicles in high-density cotton. Field Crops Res., 283, 108546. https://doi.org/10.1016/j.fcr.2022.108546
Zang, Y., Xiuyan, G., Zhiyan, Z., Xiwen, L., Yu, Z., Xingyuan, Q.,... Cheng, Y. (2016). Review of tensairity and its applications in agricultural aviation. Int. J. Agric. Biol. Eng., 9(3), 1-14.
Zhan, Y., Chen, P., Xu, W., Chen, S., Han, Y., Lan, Y., & Wang, G. (2022). Influence of the downwash airflow distribution characteristics of a plant protection UAV on spray deposit distribution. Biosyst. Eng., 216, 32-45. https://doi.org/10.1016/j.biosystemseng.2022.01.016
Zhang, B., Tang, Q., Chen, L.-p., Zhang, R.-r., & Xu, M. (2018). Numerical simulation of spray drift and deposition from a crop spraying aircraft using a CFD approach. Biosyst. Eng., 166, 184-199. https://doi.org/10.1016/j.biosystemseng.2017.11.017
Zhang, D., Chen, L., Zhang, R., Hoffmann, C., Xu, G., Lan, Y.,... Xu, M. (2015). Evaluating effective swath width and droplet distribution of aerial spraying systems on M-18B and Thrush 510G airplanes. Int. J. Agric. Biol. Eng., 8(2), 21-30.
Zhang, D., Lan, Y., Chen, L., Wang, X., & Liang, D. (2014). Current status and future trends of agricultural aerial spraying technology in China. Trans. CSAM, 45, 53-59.
Zhang, H., Qi, L., Wan, J., Musiu, E. M., Zhou, J., Lu, Z., & Wang, P. (2022). Numerical simulation of downwash airflow distribution inside tree canopies of an apple orchard from a multirotor unmanned aerial vehicle (UAV) sprayer. Comput. Electron. Agric., 195, 106817. https://doi.org/10.1016/j.compag.2022.106817
Zhang, X., & Xiong, L. (2021). Effect of adjuvants on the spray droplet size of pesticide dilute emulsion. Colloids Surf., A, 619, 126557. https://doi.org/10.1016/j.colsurfa.2021.126557
Zhang, X.-Q., Song, X.-P., Liang, Y.-J., Qin, Z.-Q., Zhang, B.-Q., Wei, J.-J.,... Wu, J.-M. (2020). Effects of spray parameters of drone on the droplet deposition in sugarcane canopy. Sugar Tech, 22(4), 583-588. https://doi.org/10.1007/s12355-019-00792-z