Sustainable phytoprotection: a smart monitoring and recommendation framework using Puma Optimization for potato pathogen detection

Ensuring sustainable and resilient agricultural systems in the face of intensifying crop disease threats requires intelligent, data-driven tools for early detection and intervention. This study proposes a novel hybrid framework for potato disease classification that integrates copula-based dependency modeling with a Restricted Boltzmann Machine (RBM), further enhanced through hyperparameter tuning using the biologically inspired Puma Optimization (PO) algorithm. The system is trained and evaluated on a real-world dataset derived from structured field experiments, comprising 52 instances and 42 agronomic, microbial, and ecological variables. By fusing copulabased transformations with PO-driven optimization, the framework effectively models complex nonlinear dependencies among heterogeneous features, enabling high-fidelity probabilistic inference in high-dimensional ecological spaces. The RBM baseline outperformed conventional classifiers such as KNN, Random Forest, XGBoost, and MLP, achieving 94.77% accuracy. With PO-based optimization, performance improved significantly to 98.54% accuracy, with parallel gains in sensitivity, specificity, and F1-score. Statistical analysis using ANOVA and Wilcoxon signed-rank testing confirmed the significance of these improvements (p <0.002). In contrast, convergence analysis demonstrated PO-RBM's computational efficiency relative to PSO, GWO, and GA alternatives. These findings underscore the utility of the proposed framework as a scalable and ecologically grounded decision-support system for integrated pest management (IPM), offering a practical path toward low-impact, adaptive plant health monitoring solutions.