Game Theory-Infused Hybrid CatBoost- Extreme Learning Machine model for Reliable Identification of Rice Leaf Diseases for Advancing Agricultural Surveillance

The global economy greatly relies on rice cultivation, yet the agricultural sector is primarily challenged by the prevalence of rice leaf diseases. This research introduces a novel Game Theory-Infused Hybrid CatBoost-Extreme Learning Machine (GT-CBELM) model tailored for the accurate and dependable detection of rice leaf diseases, thereby advancing agricultural surveillance practices. The proposed methodology harnesses cutting-edge image pre-processing methods, Contrast-Limited Adaptive Histogram Equalization (CLAHE), to enhance image quality and reveal critical disease-related details. The Grab Cut algorithm, achieves refined segmentation of disease-affected regions, leading to focused feature extraction and a substantial improvement in disease classification accuracy. Texture-specific features are extracted using the Grey Level Cooccurrence Matrix (GLCM) technique, effectively capturing essential structural information from affected areas. A groundbreaking contribution lies in the integration of game theory-based feature selection, empowered by the Banzhaf power index, which adeptly identifies relevant features while accounting for their inherent interdependencies, mitigating overfitting concerns and enhancing generalization capabilities. By seamlessly merging Game Theory with CatBoost algorithm’s robust categorical feature handling and ELM's pattern recognition process, the hybrid model excels in classifying three distinct rice leaf diseases brown spot, bacterial leaf blight, and leaf smut with remarkable precision and reliability. This innovative approach holds great promise for revolutionizing agricultural management strategies by enabling immediate and accurate disease identification, thereby contributing to enhanced crop health and agricultural productivity.