Assessment of off-road agricultural traction in situ using large scale machine learning and neurocomputing models
摘要
Artificial neuro-cognitive models can simulate human brain intelligence to enable accurate decision-making during complex agricultural operations in situ. To investigate this, twelve machine learning algorithms were employed to sequentially train 72 neurocomputing architectures using Deep Neural Network (DNN) and Artificial Neural Network (ANN) models for the neurocognitive prediction of tractive force (FTr). Fourteen soil-machine input variables were used, and the hyperparameters of the neuro-cognitive models were optimized through metaheuristic algorithms, targeting 50,000 neuro-perceptron epochs to minimize convergence error. The performance of the neurocomputing models was evaluated using standard accuracy metrics, including coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and prediction accuracy (PA). The DNN Levenberg–Marquardt (trainlm) model with a 14-7-5-1 architecture demonstrated superior predictive performance (RMSE = 1.03e-5, R² = 1.0, MAE = 8.0e-6, and PA = 99.994), closely followed by the ANN Bayesian Regularization (trainbr) model with a 14-72-1 architecture (RMSE = 4.0e-4, R² = 0.9999, MAE = 0.0002, and PA = 99.935). Although the DNN trainlm model required slightly more epochs to reach optimal performance (55 vs. 51), it achieved faster computation (2s vs. 29s) than the ANN trainbr model. The reliability indices, i.e., a20-index (a20), scatter index (IOS), and agreement index (IOA), revealed that the DNN trainlm (14-7-5-1) and ANN trainbr (14-72-1) models are highly reliable. Notably, the ANN trainbr model attained the highest