@Article{AbrantesQuLuMeKuBrMe:2021:AsEfDi,
               author = "Abrantes, Tales Camargos and Queiroz, Andrew Rerison Silva and 
                         Lucio, Felipe Ridolfo and Mendes J{\'u}nior, Cl{\'a}udio Wilson 
                         and Kuplich, Tatiana Mora and Bredemeier, Christian and Merotto 
                         J{\'u}nior, Aldo",
          affiliation = "{Universidade Federal do Rio Grande do Sul (UFRGS)} and 
                         {Universidade Federal do Rio Grande do Sul (UFRGS)} and 
                         Agriculture Division of DowDuPont, Corteva Agriscience and 
                         {Universidade Federal do Rio Grande do Sul (UFRGS)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         do Rio Grande do Sul (UFRGS)} and {Universidade Federal do Rio 
                         Grande do Sul (UFRGS)}",
                title = "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",
              journal = "International Journal of Remote Sensing",
                 year = "2021",
               volume = "42",
               number = "7",
                pages = "2740--2758",
             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.",
                  doi = "10.1080/01431161.2020.1832283",
                  url = "http://dx.doi.org/10.1080/01431161.2020.1832283",
                 issn = "0143-1161",
             language = "en",
           targetfile = "abrantes_2021.pdf",
        urlaccessdate = "31 maio 2024"
}