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

Abstract

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