Estimating peanut invisible losses using machine learning and remote sensing - Brito Filho, Armando L. de; Carneiro, Franciele M.; Almeida, Samira L. H. de; Ferreira, Francielle M.; Shiratsuchi, Luciano S.; Silva, Rouverson P. da (Anais do Congresso Brasileiro de Agroinformática (SBIAGRO), 2023)
Monitoring losses of underground crops still needs to be studied, mainly for estimating losses using new techniques that facilitate the farmer's decision-making. This study aimed to estimate peanut invisible losses using remote sensing and machine learning. The experimental area was conducted in a peanut agricultural region near Taquaritinga in São Paulo, Brazil. Seventy sample points were collected, encompassing various aspects of peanut losses, including visible and invisible losses, yield, maturation, and orbital remote sensing data. The statistical analysis used were Principal Components Analysis and Random Forest. The study concluded that the best results were RGB, RE + NIR, NIR + R, and RE to estimate peanut invisible losses.