Classification of cotton crop disease using hybrid model and MDFC feature extraction method

Abstract

A novel Modified Deep Fuzzy Clustering (MDFC) based classification model involves four major phases. They are preprocessing, segmentation, feature extraction and finally, detection and classification phase. To reduce noise and smooth the edges of the input image of the cotton crop, bilateral filtering is first used as a preprocessing approach. Next, a modified deep fuzzy clustering is suggested for the segmentation procedure that creates a collection of segments from the preprocessed image. The segmented image is then processed to extract relevant characteristics by using an enhanced Pyramid of Histogram Orientation Gradient (PHOG), Local Directional Ternary Pattern (LDTP), and statistical-based features. In order to detect and classify cotton crop diseases more effectively, this paper proposes a hybrid system. Here, the features are put through a detection phase, after which the extracted features are trained in the Bidirectional Gated Recurrent Unit (Bi-GRU) model to determine whether or not the cotton crop is infected. Once it is detected to be diseased, the type of disease is classified via an improved Recurrent Neural Network (RNN). In terms of several performance metrics, the proposed model is validated in comparison with the traditional approaches. The MDFC-based classification model outperforms existing models with a specificity of 0.9687 at a learning rate of 90. In contrast, other models achieve lower specificities: Bi-GRU (0.8436), RNN (0.8359), CNN (0.8654), LSTM (0.8769), SVM (0.7983), VGG16 (0.8619), DCNN (0.8725), BI-RNN + BI-LSTM (0.7869), and NN + CNN (0.85478).