Biophysical characteristics of soybean estimated by remote sensing associated with artificial intelligence

Franciele Morlin Carneiro; Mailson Freire de Oliveira; Samira Luns Hatum de Almeida; Armando Lopes de Brito Filho; Carlos Eduardo  Angeli Furlani; Glauco de Souza Rolim; Antonio Sergio Ferraudo; Rouverson Pereira da Silva

doi:10.14393/BJ-v38n0a2022-55925

Authors

Franciele Morlin Carneiro Louisiana State University
Mailson Freire de Oliveira Universidade Estadual Paulista https://orcid.org/0000-0003-4771-0424
Samira Luns Hatum de Almeida Universidade Estadual Paulista https://orcid.org/0000-0001-6900-1616
Armando Lopes de Brito Filho Universidade Estadual Paulista
Carlos Eduardo Angeli Furlani Universidade Estadual Paulista https://orcid.org/0000-0002-1508-5372
Glauco de Souza Rolim Universidade Estadual Paulista
Antonio Sergio Ferraudo Universidade Estadual Paulista
Rouverson Pereira da Silva Universidade Estadual Paulista https://orcid.org/0000-0001-8852-2548

DOI:

https://doi.org/10.14393/BJ-v38n0a2022-55925

Keywords:

Artificial Neural Networks, Active Optical Sensor, Glycine max L. Machine Learning., Vegetation Index.

Abstract

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.

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