Plant Leaf Disease Detection for Sustainable Crop Monitoring Using CNN-Based Approaches

Abstract

Global agricultural sustainability and food security are increasingly challenged by pervasive crop diseases, necessitating advanced, real-time detection systems. Existing methodologies often struggle with the limitations of visual symptom reliance, generalizability across diverse crop types, and integration into continuous monitoring paradigms. This paper proposes a comprehensive framework for sustainable crop health monitoring that integrates cutting-edge Convolutional Neural Network (CNN) architectures with multimodal sensing techniques. We highlight the strategic integration of signals from diverse sources, such as RGB, thermal, and hyperspectral imagery, to record subtle physiological biomarkers that signal early disease onset, transcending traditional visual diagnostics. Our experimental validations establish the superiority of the framework's performance at a remarkable 96.8% accuracy and an important 91.3% early disease detection rate. This far surpasses current unimodal methods, which generally have an early detection rate of just 65.2% for a standard RGB CNN. By promoting an astute, evidence-based method to crop disease management, this work makes a valuable contribution to increasing agricultural resilience, improving resource efficiency, and promoting environmentally conscious agriculture globally. It demonstrates its effectiveness in detecting diseases at their earliest stages.

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