A novel image classification technique for spot and blight diseases in plant leaves
Aditya Sinha, Rajveer Singh Shekhawat
Abstract
Plant disease classification using image processing techniques is a prominent and challenging area of research. We have developed a novel classification technique to classify, especially spot and blight diseased leaf images of four different plant species. In this technique, we have dealt with the infection patterns manifested on leaves. The infection patterns seem to correlate with diseases. Both these diseases cause similar patterns on leaves, and hence they are hard to distinguish. The proposed technique succeeded in handling the task to a reasonable extent. Statistical texture features derived from Grey-Level-Co-occurrence-Matrix (GLCM) are considered as features. The final feature set contains strongly correlated features. An impact level of each feature is derived from its standard deviation for the image set. The novel classification technique makes use of these impact levels. A 74% disease classification accuracy is achieved in the best-case scenario and identified an optimal threshold range that helps us classify the diseases.