A novel Moore-Penrose pseudo-inverse weight-based Deep Convolution Neural Network for bacterial leaf blight disease detection system in rice plant. (December 2022) - Daniya, T.; Vigneshwari, S.
Highlights: This paper proposed Moore-Penrose pseudo-inverse Weight-related Deep Convolutional Neural Network (MPW-DCNN). Input image are mitigated by Hybrid Gaussian-Weiner filter, and the image pixel is normalized using min-max normalization. The segmentation of the input image is done by Improved Fuzzy C-Means (IFCM). The efficient features are chosen by Exhaustiveness and Brownian Motion-related Elephant Herding Optimization (EBM-EHO). The proposed MPW-DCNN performance is equated with the prevailing classification algorithm. Abstract: The vital food crop in the agriculture field is rice, but rice growing is impacted by numerous maladies. The Bacterial Leaf Blight (BLB) disease influences rice standards. Prevailing research practices have less accuracy and could not surmount the noise of the images. This paper proposed Moore-Penrose pseudo-inverse Weight-related Deep Convolutional Neural Network (MPW-DCNN) overcomes such complications for the BLB disease identification system. The proposed method encompasses six phases. First, the Image Acquisition (IA) procedure is accomplished, next the pre-processing is carried out, in which the noise of the input rice leaf image is mitigated using the Hybrid Gaussian-Weiner (HGW) filter, and the image pixel is normalized by utilizing min-max normalization. Then, the segmentation of the input image is done by Improved Fuzzy C-Means (IFCM). Next, the features, such as entropy, energy, correlation, contrast, homogeneity, colour histogram, and Scale-Invariant Feature Transforms (SIFT) are extracted from the segmented image. Then, the efficient features are chosen by applying the Exhaustiveness and Brownian Motion-related Elephant Herding Optimization (EBM-EHO) algorithm. Then, these selected features are fed to the MPW-DCNN classifier, which categorizes the image as 'BLB malady' or 'normal' or else 'chances being influenced by other maladies'. Lastly, the proposed MPW-DCNN performance is equated with the prevailing classification algorithm, and better accuracy is proffered by the proposed work than the prevalent algorithms. The accuracy of the implemented approach is 2.36, 3.08, 4.62, 5.13, and 6.15% improved than the existing methods, such as Support Vector Machine (SVM) + Deep features, AlexNet, Deep Convolutional Neural Network (DCNN), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), respectively.