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

Game Theory-Infused Hybrid CatBoost- Extreme Learning Machine model for Reliable Identification of Rice Leaf Diseases for Advancing Agricultural Surveillance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Game Theory-Infused Hybrid CatBoost- Extreme Learning Machine model for Reliable Identification of Rice Leaf Diseases for Advancing Agricultural Surveillance V. Krishna Pratap, N. Suresh Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3996107/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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. CatBoost Detection Extreme Learning Machine Game Theory Rice Leaf disease Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3996107","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276962415,"identity":"5c5097b1-3992-47d5-98ce-5929c98917da","order_by":0,"name":"V. 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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. 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