Toward Intelligent Cauliflower Disease Recognition: A CLAHE-Enhanced Image Processing and SVM-Based Classification Framework

1. Proposes an automated cauliflower disease recognition framework by integrating image processing techniques with ML for accurate disease analysis.
2. Develops a robust feature extraction pipeline using segmentation and GLCM-based texture features to enhance disease region representation.
3. Utilizes a SVM classifier for effective multi-class classification, validated through comprehensive experiments on a cauliflower disease image dataset.