Multiple Grapes Leaf Disease Identification Using an Optimal Deep Learning Model: Xception

One of the popular fruit yields in India is the grape. The spread of numerous diseases on grapes’ fruit, stem, and leaves causes a decline in production. Bacteria, fungi, viruses, etc. are the principal culprits behind leaf diseases. Diseases are a significant influence in restricting the yield of fruit, and they are frequently challenging to control. Correct control measures cannot be implemented at the right time for a disease without an accurate illness identification. One of the most popular methods for identifying and categorizing plant leaf infections is image processing. This study uses the Xception classification approach to help identify and categorize grape leaf diseases and datasets taken from an online source like Kaggle. There are a total of 8500 images of grape leaves to use in this research. Furthermore, two alternative methods KNN and SVM were evaluated in terms of their effectiveness in detecting illnesses in grape plants. The suggested Research model has an accuracy of 99% for detecting and classifying the tested grape leaf disease which has higher accuracy as compared to alternative models.