Agriculture is the backbone of Indian economy. Plant disease which mainly affects the leaves is the major constraining factor, which decreases the productivity of cucumber. Farmers are experiencing heavy loss in the yield due to disease attack on leaves. Hence detection and diagnosis of cumber leaf disease at the right time are very essential. Diagnosis of cucumber leaf disease at the early stage helps in preventing heavy loss in the yield. Automatic detection of cucumber disease using image processing techniques helps in monitoring large fields by identifying the diseases as soon as they appear on the leaf. The main purpose of this work is disease identification and classification using image processing techniques. The proposed method mainly comprises of image pre-processing, segmentation using K means clustering to segment the diseased leaf then feature extraction and followed by classification of disease using SRC. The experimental results show that the cumber leaf diseases can be identified more accurately for the proposed work.

Cucumber leaf disease, K-means Clustering, Sparse representation Classification (SRC)

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