Using a manual multispectral sensor and UAV in monitoring soybean development and productivity under rainfed conditions

Abstract

Soybean (Glycine max L.) is one of the leading cultivated crops globally. Although the region of Vojvodina is favorable for soybean production, the climate, especially high temperatures and uneven distribution of precipitation, represents a major limiting factor. The aim of this study is to investigate the correlation between soybean yield, water stress levels, and vegetation indices obtained using a handheld multispectral sensor and a drone under natural moisture conditions on a test plot in Čenej, Vojvodina. The results showed a significant correlation between vegetation indices with evapotranspiration, soil moisture changes, and soybean yield. During the intensive growth phase (V4), NDVI-UAV, EVI-UAV, and GNDVI-UAV showed highly significant positive correlations with yield (r=0.96**, r=0.94**, r=0.86*). During the flowering phase (R1), GNDVI-POM had significant positive correlations with all analyzed parameters, while GNDVI-UAV had significant correlations with evapotranspiration and soil moisture. During the pod formation phase (R3), GNDVI-UAV again showed a significant correlation with yield (r=0.86*), while NDVI-POM had significant correlations with evapotranspiration and soil moisture. During the pod filling phase (R4), EVI-UAV showed highly significant positive correlations with evapotranspiration, soil moisture, and yield (r=0.94**, r=0.96**, r=0.89**). These results are useful for the application of multispectral sensors in detecting soybean water availability and improving production under natural moisture conditions