Automatic feature extraction and detection of plant leaf disease using GLCM features and convolutional neural networks

Abstract

Agricultural productivity plays a key role in determining the economy of a country. Detection of plant leaf disease is a vital task as it greatly affects the agricultural productivity. If proper detection is not done, it may lead to serious damage in the quality and quantity of the agricultural yield. In this research we propose a novel scheme for the detection of plant leaf diseases using deep convolutional neural networks (DCNN). In the proposed framework, initially, the plant leaf images are preprocessed using filtering and enhancement techniques. In our work, image filtering is done using 2D Adaptive Anisotropic Diffusion Filter (2D AADF) for noise removal. Using these de-noised images, enhancement is done using Adaptive Mean Adjustment (AMA) technique. This step helps to intensify the region of interest in the image. Using the enhanced image, segmentation is performed by means of clustering and thresholding. Clustering is done using the Improved Fast Fuzzy C Means Clustering (IFFCMC) Algorithm and image thresholding is performed using the Adaptive Otsu (AO) thresholding algorithm. From the segmented images, features are extracted using grey level co-occurrence matrix (GLCM). Dimensionality reduction of features is performed using principle component analysis (PCA). Finally, classification is done using a novel DCNN architecture. The proposed architecture has four convolutional layers, two fully connected layers and one SoftMax layer. Experimental results show that the proposed framework is effective and achieves best classification results other classifiers.