Utilizing MATLAB For Visual Detection Of Leaf Diseases Through Image Processing

Abstract

Automated plant disease monitoring is essential for improving crop yields as it offers several advantages over manual methods. Manual inspections, which rely on visual observation, are inefficient and time-consuming. They often require experts to identify diseases accurately, making them less accessible to all farmers. In our study, we introduce a contemporary approach to disease detection in both plant leaves and fruits. We leverage digital image processing techniques, which allow us to analyze large volumes of plant images quickly and accurately. This technology overcomes the limitations of traditional visual inspection methods, as it can detect subtle disease symptoms that may not be apparent to the human eye. Our system is designed to combine the power of the k-means clustering algorithm with a multi-SVM (Support Vector Machine) approach. This combination allows for precise disease identification and classification. MATLAB software is used to implement this system, making it accessible and user-friendly for farmers and researchers alike. Overall, our approach represents a significant step forward in the field of plant disease monitoring, offering a faster, more accurate, and accessible solution for identifying and managing diseases in crops. This innovation has the potential to improve crop health, increase yields, and ultimately benefit agriculture and food production.