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UAV Multispectral Imagery for Site-Specific Management of Iron Deficiency Chlorosis (IDC) in Soybean

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

Citation:  2022 ASABE Annual International Meeting  2201111.(doi:10.13031/aim.202201111)
Authors:   Katelin S. Waldrep, Mary Love M. Tagert, Justin McCoy, Mark Harrison, Andy Taylor
Keywords:   iron deficiency chlorosis (IDC), leaf area index (LAI), management zones, normalized difference vegetation index (NDVI), unmanned aerial vehicle (UAV).

Abstract. Iron deficiency chlorosis (IDC) of soybeans is a problem throughout many areas of the United States, including Mississippi‘s Blackland Prairie region. While a tolerant variety can increase yield within a chlorosis-prone area, these varieties often result in lower yields when planted in asymptomatic areas of the same field. Precision planting has potential to alleviate this issue by planting a more tolerant variety in field areas identified as IDC-prone and a higher yielding - but more susceptible - variety in areas not affected. Little to no research has been conducted on site-specific management of IDC and associated potential yield benefits. This project was established in 2019 at the North Mississippi Research and Extension Center as a split-plot design with seven different cropping systems as main plots and six soybean varieties as subplots. Each subplot was evaluated on IDC visual ratings and chlorophyll content, and multispectral imagery via unmanned aerial vehicle (UAV) was collected weekly (weather permitting) over the study area. Leaf area index (LAI) was recorded weekly on two varieties in each cropping system, and LAI values were used to calculate normalized difference vegetation index (NDVI) for comparison to image-derived NDVI. Visual ratings were also compared to both calculated and image-derived NDVI. For the July 22, 2021 sampling date, there was good correlation between calculated and image-derived NDVI (R2 = 0.6229) but not as good between visual ratings and calculated and image-derived NDVI. Results will be used to delineate IDC management zones within a production field during the 2022 growing season.

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