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 \: \: ₓ₎ₓ₀₋ (= 240), while the DNN trainlm model was the most reliable under its configuration (\: \: ₓ₎ₓ₀₋ = 196). Taylor's analysis revealed no statistically significant deviation between the experimental and predicted FTr values for both models. Furthermore, all prediction instances (100%) for both trainbr (14-72-1) and trainlm (14-7-5-1) fell within the 95% prediction uncertainty range (PPU95%), with a near-zero neurocognitive uncertainty index (\: -dUx = 0. 00;0. 03) and minimal logical deviation factors (dfactor=0. 03;1. 58), confirming Monte Carlo consistency between predicted and observed FTr values. The Anderson–Darling test confirmed the normality of the predictions (\: P₌₎₃₄₋ = 0. 001), with both models satisfying the condition (Pmodel< Pideal, 0. 05) . Finally, the ANN model under trainbr also achieved excellent performance. The DNN trainlm (14-7-5-1) model employed a log-sig–tan-sig–purelin activation sequence, whereas the ANN trainbr (14-72-1) model used a tan-sig and purelin configuration, both suitable for in-situ prediction of FTr. In addition, it was observed that the Spider Wasp Optimization (SWO) algorithm enhanced the performance of the conventional DNN (trainlm) and ANN (trainbr) models in predicting agricultural traction.
Mwiti et al. (Fri,) studied this question.