Intelligent Leaf Disease Diagnosis: Fuzzy Logic and CNN-Based Multi-Class Detection

Abstract

Introduction: This paper presents an approach for identifying and classifying plant leaf diseases through the use of Convolutional Neural Networks (CNNs) augmented with Fuzzy Logic techniques. The proposed system measures disease severity and suggests treatment options. The foundation of this research lies in a diverse raw image dataset of rice, wheat, corn, sugarcane, maize, barley, and jowar leaves, ensuring robustness and real-world applicability. Our approach leverages an advanced Convolutional Neural Network (CNN) architecture for precise plant disease classification, while incorporating fuzzy logic to perform a detailed analysis of disease severity. We present experimental results along with a thorough comparison to current state-of-the-art methods, highlighting improvements in detection accuracy, processing efficiency, and multi-class classification performance. This integrated framework demonstrates its effectiveness in agricultural disease management, significantly reducing crop losses. Furthermore, this research contributes to the field by providing a comprehensive solution that not only detects and classifies plant diseases but also offers actionable insights for targeted interventions to optimize crop health and productivity