Assessing the effects of dicamba and 2,4 Dichlorophenoxyacetic acid (2,4D) on soybean through vegetation indices derived from Unmanned Aerial Vehicle (UAV) based RGB imagery

The increase in agricultural production is facing several challenges with future implications for food security and environmental protection. aim of this study was to evaluate a remote sensing-based low-cost methodology assessing the effects dicamba 2,4 Dichlorophenoxyacetic acid (2,4D) non-tolerant soybean crop. Here, we introduced application six vegetation indices (VI) derived from Unmanned Aerial Vehicle (UAV) based Red-Green-Blue (RGB) imagery contrasting conventional approach visual injury criteria classification estimate plant effect on grain yield. results demonstrated feasibility Modified Green-Red Vegetation Index (MGRVI) Excess Green (ExG) strongly correlated 2,4D soybean. These VIs discriminated caused by up 5% recommended dose. Lethal Dose 50 (LD50) considering yield around 13% (72.80 g a.e. ha−1), 55% (552.75 ha−1) 48% (482.40 dicamba; dimethylamine (DMA) choline (CHO) dose, respectively. This revealed noteworthy limitations RGB discriminate between different formulations same herbicide, as DMA CHO. With expectations introduction new genetic events alongside synthetic auxin compounds, our pointed out that proposed can lead protocol identifying estimating damage off-target movement these outcoming herbicides neighbourhood fields.