Crop Disease and Pest Image Recognition Using MATLAB
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Resource Overview
MATLAB-based image recognition program for crop diseases and pests, trained on four severity levels, achieving high accuracy after model completion with implementation featuring image preprocessing, feature extraction, and machine learning classification.
Detailed Documentation
This case study presents a MATLAB implementation for crop disease and pest image recognition. The system trains a classification model to identify four distinct severity levels of crop infections. Through comprehensive model training incorporating image augmentation techniques and convolutional neural network (CNN) architecture, the program achieves significantly high recognition accuracy. Key algorithmic components include HSV color space conversion for lesion detection, texture analysis using GLCM (Gray-Level Co-occurrence Matrix), and SVM (Support Vector Machine) classification. This solution enables farmers to effectively monitor and address crop health issues, ultimately improving agricultural yield quality and quantity through automated early detection capabilities.
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