S. K. Shah - Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune, India
V. Kumbhar - Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune, India
T. P. Singh - Symbiosis Institute of Geoinformatics, Symbiosis International (Deemed University), Pune, India

In modern horticulture, the grape industry across the globe has been coping with the issue of grape crop diseases. The detection of grape leaf diseases using automated methods can greatly assist farmers in mitigating yield losses and ensuring sustainability. However, existing systems face hurdles while handling grape leaf images at the farm level, and these models fail to generalize well on un-seen images. This study proposes the development of a well-curated real-time dataset of grape leaf images assimilated through field visits in the study area in India. This designed dataset is further used to train convolutional neural network models to accurately identify and classify grape leaves as either diseased or healthy. The potential of transfer learning using CNN models like VGG, ResNet, Inception, and Xception is assessed on the curated dataset. Our findings indicate that ResNet۵۰V۲ outperformed the other models in accurately identifying and classifying grape leaf diseases. Using transfer learning, existing weights (pre-trained) and learned features were utilized for further training and fine-tuning the CNN models on our curated dataset.  The results of the proposed approach are compared with existing automated grape leaf disease identification techniques. It is observed that the proposed approach, which is on a real-time grape leaf image dataset, provides the highest accuracy among others. Further, this study provides a well-curated dataset of on-field grape leaf images in the Indian context, which can serve as a benchmark for future research. This study shows that deep learning techniques can aid farmers in identifying grape leaf diseases early.

Convolutional Neural Networks, Deep Learning, Disease Classification, Machine Learning, Transfer learning