Prediction of Areal Soybean Lodging Using a Main Stem Elongation Model and a Soil-Adjusted Vegetation Index That Accounts for the Ratio of Vegetation Cover

In soybean, lodging is sometimes caused by strong winds and rains, resulting in a decrease yield quality. Technical measures against include “pinching”, which the main stem pruned when excessive growth expected. However, there can be pinching undertaken risk of relatively low. Therefore, it important that performed after future has been determined. The angle at full maturity stage (R8) explained using multiple regression model with elongation from sixth leaf (V6) to blooming (R1) length seed (R6) as explanatory variables. objective this study was develop an areal prediction method combining estimation UAV remote sensing. emergence R1 logistic formula temperature daylight hours functions f (Ti, Di) peak linear variable. synthesized these two formulas were used R8. accuracy tested on test data, average RMSE 5.3. For sensing, we proposed soil-adjusted vegetation index (SAVIvc) takes cover into account. SAVIvc more accurate estimating than previously reported (R2 = 0.78, p < 0.001). estimated combined substituted method. able predict angles 8.8. These results suggest manner prior pinching, even though actual occurrence affected wind.