Plant Leaf Disease Classification in Precision Farming With Hybrid Classifier: Colour, Deep and Pattern-Based Feature Descriptors

Abstract

In the agricultural sector, pesticides are used to prevent disease transmission and protect crop yields. However, due to the diverse range of diseases, the human observation can often lead to misidentification. It is essential for a timely and precise disease classification approach without human intervention. Classifying the plant leaf diseases with an automated system is the significant need in this scenario. In this work, a hybrid classification model for the categorisation of plant leaf diseases is presented. Preprocessing, segmentation, feature extraction and classification of leaf diseases are the four steps in this method. In this work, crops such as grapes and mango are considered. Primarily, preprocessing the input image by utilising Gaussian filtering methods, which enhances the quality of image. The filtered image is then put through a segmentation process using the MBIRCH framework. The segmented image is then used to extract a number of features, including GLCM, ILGBHS, colour, shape and deep features using the VGG16 and AlexNet networks. Following the procedure, the hybrid model—which combines Bi-GRU and DCNN with TL—is applied to the acquired features, and the final classified result is determined by the enhanced fusion score method.