Application of artificial intelligence for identification of peanut maturity using climatic variables and vegetation indices - Barboza, Thiago Orlando Costa; Souza, Jarlyson Brunno Costa [UNESP]; Ferraz, Marcelo Araújo Junqueira; de Almeida, Samira Luns Hatum [UNESP]; Pilon, Cristiane; Vellidis, George; da Silva, Rouverson Pereira [UNESP]; dos Santos, Adão Felipe (Springer Nature, 2025)
Purpose The hull scrape and vegetation indices are widely used for predicting peanut maturation, but they are time-consuming, subjective, labor-intensive, and fail to account for climate variables, reducing their accuracy.Thus, the objective was to verify the potential of using artificial intelligence associating IV and climate variables to predict the variability of peanut pod maturity in the fieldMethods For this purpose, peanut maturity data collected on different dates in commercial fields in Brazil and the United States. In addition, high-resolution satellite images were used to calculate nine IV and four climatic variables for each area were acquired using the NASA-POWER platform. Four machine learning models were tested and the input for the training were selected using the Random Forest feature selection. Thus, the models were trained using 70% of the data for training and 30% for testing and applied the cross validation with K-fold.ResultsThe best results were obtained for the XGBoosting model with R2 test values varying 0.90, 0.89, 0.93 and 0.87 and a minimum MAE and RMSE of 0.05. Except for the Georgia dataset where the MLP model presents the highest performance R2 value of 0.93, MAE 0.05 and RMSE 0.06 for the test. The RBF models present the worst results with a low index of agreement (d) 0.4 for all the datasets demonstrating a low agreement between the predicted and observed values.Conclusion Combining the climatic variables was able to improve the model’s performance, however detailed information about the field such as topographic conditions and soil type seem to be a different approach to enhance the model performance. Using the calibrated model for overall dataset peanut farmers from any localities can use to monitor and map the PMI variability in the fields, improve the decision-making, decrease the loss and increase the kernels quality.