Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

A review of the state of the art in agricultural automation. Part IV: Sensor-based nitrogen management technologies

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2018 ASABE Annual International Meeting  1801593.(doi:10.13031/aim.201801593)
Authors:   Diogenes L. Antille, Craig R. Lobsey, Cheryl L. McCarthy, J. Alex Thomasson, Craig P. Baillie
Keywords:   Machine vision, Optical plant sensors, Sensor-carrying platforms, 3D-imaging, unmanned aerial vehicle (UAV), unmanned ground vehicles (UGV), vis-NIR.

Abstract. Crop nitrogen (N) management is one of many important agricultural applications that can benefit from crop sensing. The technologies in this field are advancing rapidly, including: (1) sensor-carrying platforms, (2) the sensors themselves, and (3) the analytical techniques used to derive actionable information from the data. A review of commercially and semi-commercially available platforms was undertaken to inform sensor mounting, with particular focus on unmanned aerial vehicle (UAV) sensor platforms and unmanned ground vehicle (UGV) sensor platforms. The UAV and UAG platforms provide indirect and direct measurements for crop monitoring and N mapping with the goals of being low-cost, on-site, and versatile. Optical crop sensing techniques and systems for N management are also discussed, because destructive sampling and laboratory analyses are expensive and often not practical for site-specific management of N. The optical properties of the plant are significant because they are related to water content, leaf senescence, disease, and nutrient status, which can inform farming decisions. Additionally, Red, Green and Blue (RGB) imaging can provide a plant height assessment for multiple measurements, including: yield potential, biomass, density, uniformity, and planter skips. The work reported in this paper includes a comparison of various optical sensors for plant measurements, including: vis-NIR, Machine Vision, and 3D-imaging, with camera varieties such as multispectral, fluorescence, hyperspectral, thermal, and visible. Key recommendations have been provided for the development of data aggregation and decision support tools including the data sources to be used in development of machine learning models, software/data standardization efforts, and corporate collaborations regarding big data. In conjunction with the sensors and their platforms, this advancing field of management technology can provide intelligent sensing and intelligent decisions.

(Download PDF)    (Export to EndNotes)