Anthracnose Disease Detection in Cashew Leaf Using Machine Learning Technique Based on Contour Detection and Principal Component Analysis

Detecting and classifying leaf diseases in cashew crops is critical for farm- ers to find pest disease infections. Cashew can reduce pro- ductivity if not detected early. Creating an automated method utilizing image processing identification decreases time expense pri- marily contributes a rise nut yield. For segmentation, canny edge detection active contour model are utilized. A feature extraction method, Principal Component Analysis (PCA), applied when the has been applied. After features have extracted, they submitted categorization. This study analyzed several classifiers’ accuracy, preci- sion, recall values. These classifiers included Random Forest, SVM, KNN, Naive Bayes. research tries answer whether machine learning classifier provides best results diseased area divided using technique.