An Enhanced Identification and Classification Algorithm for Plant Leaf Diseases Based on Deep Learning

Abstract

Identification of plant disease sis a difficult task for farmers. If the diseases are misidentified, there will be a huge crop failure, which threatens the living of farmers. This paper proposes a new tool for farmers to identify plant leaf diseases automatically, and provide solutions to this problem on expert database. Firstly, the infected spots of the leaf are recognized through fuzzy c-means clustering (FCM). Then, the features are extracted by gray-level cooccurrence matrix (COLCM), and classified by progressive neural architecture search (PNAS). The proposed tool was tested on Mendeley Dataset, which covers 2,278 images of healthy leaves, and 2,225 leaves with leaf blights, rust, mealy bugs, and powderily mildew, angular leaf spot, and downy mildew. The experimental results show that our approach outperformed the other methods in accuracy (up to 95%).