Revolutionizing Rice Agriculture: A Machine Learning Approach to Fungal Disease Management for Economic Sustainability

This research presents an innovative Decision Support System (DSS) utilizing machine learning (ML) techniques, tailored for the effective control of fungal diseases in rice farming, an essential component of global food security. Rice, a cornerstone in sustaining billions, grapples with persistent threats from diverse fungal infections, leading to substantial losses in yield and quality. Our innovative DSS harnesses ML techniques, drawing from an extensive dataset encompassing images and metadata related to various fungal diseases affecting rice plants. Engineered for real-time functionality, our DSS furnishes swift and precise diagnoses coupled with tailored management recommendations, elevating the efficacy of crop disease mitigation. Rigorously trained on a diverse dataset, the system exhibits impressive accuracy, validated through meticulous evaluations on a dedicated test dataset containing images of rice plants afflicted with known fungal infections. The practical ramifications of our ML-based DSS are profound, holding the potential to transform rice farming practices and make substantial contributions to global food security. By delivering timely and accurate diagnoses, the system empowers rice farmers and agricultural extension workers to promptly implement targeted management strategies, thus minimizing losses and optimizing yields. This user-friendly tool, positioned at the nexus of technology and agriculture, aims to streamline decision-making processes, diminish reliance on traditional methods, and fortify the resilience of rice crops against fungal diseases. As a beacon of innovation in crop disease management, our ML-based DSS promises to be an invaluable resource for the agricultural community. By augmenting the capabilities of those at the forefront of rice cultivation, it ensures not only sustainable food production but also underscores the transformative potential of advanced technology in addressing pressing challenges within global agriculture.