Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network

Abstract

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The progress made in deep learning techniques has significantly impacted agricultural disease diagnostics. Rice Plant leaf diseases can have severe adverse effects on crop yield, and proper diagnosis of Rice Plant diseases is vital to avoid these effects. Most of the existing methods do not adequately detect the leaf images and cannot find the condition accurately. Previously, there were some problems with leaf segmentation. Complex disease stages are efficient but computationally time-consuming, and segmentation needs to be more accurate, affordable, and reliable. To overcome the issues, the proposed method of Deep Spectral Generative Adversarial Neural Network (DSGAN2) with Improved Artificial Plant Optimization for Rice Plant leaf disease detection. Initially, we fed into the input of healthy and non-healthy leaves from the collected dataset. Then, we apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the Closest Weight from segmented Rice Plant leaves. Social Spider Optimization to select the Feature with the Closest Weight (S2O-FCW) algorithm for analysis of the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects Rice Plant disease based on selected features. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2 %, Alex Net is 50.4 %, and Convolutional Neural Network (CNN) is 49.5 %.