Peanut Loss Predictions Using Random Forest and Soil Data

  • Armando L. de Brito Filho Unesp
  • Franciele M. Carneiro UTFPR
  • Gabriel P. Costa Unesp
  • Lucas Matheus Agostini Unesp
  • Jarlyson Brunno Costa Souza Unesp
  • Rouverson P. da Silva Unesp


This work aimed to evaluate the Random Forest algorithm performance to predict mechanical digging loss of the peanut crop. Four approaches were tested: only soil texture data, soil texture + TPI, soil texture + TWI, and soil texture + TPI + TWI. The model's performance was evaluated regarding precision (coefficient of determination) and accuracy (mean absolute error). The results found in this work proved promising in predicting peanut digging losses. Our models achieve results with an approximate 100 kg ha-1 prediction error. In addition, incorporating one or more topographical indexes in conjunction with soil texture data as features notably improves the models' precision and accuracy.


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BRITO FILHO, Armando L. de; CARNEIRO, Franciele M.; COSTA, Gabriel P.; AGOSTINI, Lucas Matheus; SOUZA, Jarlyson Brunno Costa; SILVA, Rouverson P. da. Peanut Loss Predictions Using Random Forest and Soil Data. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA (SBIAGRO), 14. , 2023, Natal/RN. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 72-79. ISSN 2177-9724. DOI: