A Novel Framework For Automatic Detection Of Plant Leaf Disease Using 2d-Deep Convolutional Neural Network Architecture

Abstract

The economy of a country largely depends on its agricultural productivity. Hence, identification of plant leaf diseases plays a vital role in the field of agriculture. In this research, we propose a novel framework for the automatic detection of plant leaf disease based on Deep Convolutional Neural Network (DCNN) architecture. The proposed framework involves steps like image restoration, enhancement, clustering, thresholding and classification. The image restoration is performed using a novel filter called 2D Adaptive Hybrid Bilateral Anisotropic Diffusion Filter (2D–AHBAD). This filter is used for the elimination of various noise such as salt and pepper noise, Gaussian noise, random noise, thermal noise, speckle noise etc. Image enhancement is done using Edge Preservation–Modified Histogram Contrast Brightness Equalization (EP-MHCBE) algorithm. The enhanced images are then segmented using clustering and thresholding algorithms. A new technique called Hybrid Fast Fuzzy C Means Improved Expectation Maximization (HFFCM IEM) Clustering technique was used for the computation of clusters. The generated clusters are then segmented based on the Iterative Mean Shift Thresholding (IMST) algorithm. The segmented images are classified using DCNN architecture. A total of 2000 images are used in this framework out of which 1600 images were utilized for training the DCNN architecture. The remaining 400 images were used for testing. The leaf images are categorized into four categories namely, normal, mild, moderate and severe. It was inferred that the proposed AHBAD image restoration algorithm achieved a high PSNR of 54 and very low MSE of 0.0039. Similarly, the proposed DCNN classification system attained a high classification accuracy of 92.25%.