Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic

Abstract

• A new disease evaluation method fusing deep learning and fuzzy logic is proposed. • DeepLabV3+ with ResNet50 backbone is utilized for images segmentation. • A fuzzy reference system is introduced for grape disease grades classification. • Two complementary indices are given to determine the disease grades. Grape black measles disease may be one of the best known, longest studied and most destructive of all plant diseases, which ultimately reduces productivity and quality of products. Timely, effective and accurate evaluation of grape black measles disease is acknowledged as a crucial step in field management. In this paper, an effective automatic detection and severity analysis method is proposed for grape black measles disease based on deep learning and fuzzy logic. In the first phase, the state-of-the-art ResNet50-based DeepLabV3+ semantic segmentation model is trained for pixel-level predictions in images of individual leaves exhibiting pathological lesions caused by fungus. In this way, the extracted features including ROI and POI are obtained. Second, for each feature, the fuzzy rule-based system is developed for predicting the harm degree of disease. Also, appropriate membership functions for the inputs and output are considered for fuzzification and defuzzification purposes in the fuzzy logic system. Finally, grape leaves are divided into grades of Healthy, Mild, Medium and Severe. Experimental results report an overall classification accuracy of 97.75% on the hold-out test dataset. It is concluded that the DeepLabV3+ model-based fuzzy reference system can be used effectively to classify grape leaves with different disease risks, based on combination of image analysis and statistical calculation.