SpringerLink

Performance evaluation of the classifiers based on features from cotton leaf images - Multimedia Tools and Applications

Cotton disease classification is required because it affects agricultural yield. Leaf disease was classified using machine learning and deep learning classifiers. The performance of the machine learning classifiers is evaluated based on the extracted features. Disease classification is possible after preprocessing, segmentation, and feature extraction stages. Preprocessing was achieved by bilateral filtering and segmentation using the modified Chan Vese method; color moment and texture features were extracted. Finally, multiple classifiers were used for disease classification. Classifier’s support vector machine, random forest, K-nearest neighbor, Naïve Bayes, and multilayer perceptron were used for classification. We evaluated the classifier’s performance based on the extracted features. We can conclude that the performance of the classifiers improves when we combine the color and texture features. The combined color moments, gray-level co-occurrence matrix, and local binary pattern features provide a higher classification accuracy than stand-alone features. In the experiment, multilayer perceptron achieved 98% accuracy compared to SVM, K-NN, Naïve Bayes, and random forest. Further, the multilayer perceptron is compared with other well known deep learning models: AlexNet, VGG 16, and ResNet. In comparison, we could observe that the ResNet model achieved an accuracy of 98.92, higher than the other models.