Crop Disease Detection by Deep Joint Segmentation and Hybrid Classification Model: A CAD-Based Agriculture Development System

Abstract

Precise detection of crop disease at the early stage is a crucial task, which will reduce the spreading of disease by taking preventive measures. The main goal of this research is to propose a hybrid classification system for detecting crop disease by utilising Modified Deep Joint (MDJ) segmentation. The detection of crop diseases involves five stages. They are data acquisition, pre-processing, segmentation, feature extraction and disease detection. In the initial stage, image data of diverse crops is gathered in the data acquisition phase. According to the work, we are considering Apple and corn crops with benchmark datasets. The input image is subjected to pre-processing by utilising the median filtering process. Subsequently, the pre-processed image under goes a segmentation process, where Modified Deep Joint segmentation is proposed in this work. From the segmented image, features like shape, colour, texture-based features and Improved Median Binary Pattern (IMBP)-based features are extracted. Finally, the extracted features are given to the hybrid classification system for identifying the crop diseases. The hybrid classification model includes Bidirectional Long Short-Term Memory (Bi-LSTM) and Deep Belief Network (DBN) classifiers. The outcome of both the classifiers is the score, which is subjected to an improved score level fusion model, which determines the final detection results. Finally, the performance of the proposed hybrid model is evaluated over existing methods for various metrics. At a training data of 90%, the proposed scheme attained an accuracy of 0.965, while conventional methods achieved less accuracy rates.