A novel computer vision based neutrosophic approach for leaf disease identification and classification. (March 2019) - Dhingra, Gittaly; Kumar, Vinay; Joshi, Hem Dutt
Highlights: Disease identification and classification of leaf using image processing. Fuzzy set extended form neutrosophic logic for evaluation of ROI. Membership functions evaluation for diseased and healthy segment. Evaluate new feature subset using texture, histogram and disease region. Monitor combined features effectiveness and accuracy using different classifiers. Abstract: The natural products are inexpensive, non-toxic, and have fewer side effects. Thus, their demand especially herbs based medical products, health products, nutritional supplements, cosmetics etc. are increasing. The quality of leafs defines the degree of excellence or a state of being free from defects, deficits, and substantial variations. Also, the diseases in leafs possess threats to the economic, and production status in the agricultural industry worldwide. The identification of disease in leafs using digital image processing, decreases the dependency on the farmers for the protection of agricultural products. So, the leaf disease detection and classification is the motivation of the proposed work. In this paper, a novel fuzzy set extended form neutrosophic logic based segmentation technique is used to evaluate the region of interest. The segmented neutrosophic image is distinguished by three membership elements: true, false and intermediate region. Based on segmented regions, new feature subset using texture, color, histogram and diseases sequence region are evaluated to identify leaf as diseased or healthy. Also, 9 different classifiers are used to monitor and demonstrate the discrimination power of combined feature effectiveness, where random forest dominates the other techniques. The proposed system is validated with 400 cases (200 healthy, 200 diseased). The proposed technique could be used as an effective tool for disease identification in leafs. A new feature set is promising and 98.4% classification accuracy is achieved.