BIOPHYSICAL CHARACTERISTICS OF SOYBEAN ESTIMATED BY REMOTE SENSING ASSOCIATED WITH ARTIFICIAL INTELLIGENCE

• Autor(a) principal: Carneiro, Franciele Morlin
• Data de Publicação: 2022
• Outros Autores: de OLIVEIRA, Mailson Freire, de ALMEIDA, Samira Luns Hatum [UNESP], de BRITO FILHO, Armando Lopes [UNESP], Furlani, Carlos Eduardo Angeli [UNESP], Rolim, Glauco de Souza [UNESP], Ferraudo, Antonio Sergio [UNESP], da SILVA, Rouverson Pereira [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.14393/BJ-v38n0a2022-55925; http://hdl.handle.net/11449/241761
• topic: Active Optical Sensor; Artificial Neural Networks; Glycine max L; Machine Learning; Vegetation Index

The biophysical characteristics of vegetative canopies, such as biomass, height, and canopy diameter, are of paramount importance for the study of the development and productive behavior of crops. Faced with a scarcity of studies aimed at estimating these parameters, the objective of this study was to evaluate the performance of artificial neural networks (ANNs) applied to Proximal Remote Sensing (PRS) to estimate biophysical characteristics of soybean culture. The data used to train and validate the ANNs came from an experiment composed of 65 plots with 30 x 30 m mesh, its development was carried out in the 2016/2017 crop in the Brazilian agricultural area. The evaluations were carried out at 30, 45, 60, and 75 days after sowing (DAS), monitoring the spatial and temporal variability of the biophysical characteristics of the soybean crop. Vegetation indexes were collected using canopy sensors. The accuracy and precision were determined by the coefficient of determination (R2) and the error of the forecasts by MAPE (Mean Absolute Percentage Error). PRS and ANNs showed high potential for application in agriculture, since they obtained good performance in the estimation of height (R2 = 0.89) and canopy diameter (R2 = 0.96), being fresh biomass (R2 =0.98) and dry biomass (R2 = 0.97) were the best-estimated variables.