1Bharati Vidyapeeth Deemed to be University, Pune-411 030, Maharashtra, India
2Bharati Vidyapeeth's Institute of Management and Technology, Mumbai University, Navi-Mumbai-400 614, Maharashtra, India
*Corresponding Author: Rasika Gajendra Patil, Bharati Vidyapeeth Deemed to be University, Pune-411 030, Maharashtra, India, Email: rasikarj.mca@gmail.com
Online published on 9 July, 2025.
In the domain of grape farming, the enduring difficulties of preventing germs and viruses pose significant risks to economic viability. Modern advances in artificial intelligence, machine learning, deep learning (AI, ML, and DL), and computer vision have produced efficient methods for identifying and classifying grape viral infections.
The development and improvement of deep learning algorithms designed particularly for identifying and categorizing grape leaf infections is the focus of this study. Utilizing deep intelligence techniques, five important predetermined Deep learning algorithms were used: Dense Net 121, VGG 19, VGG 16, Inception V3 and Res Net 50 V2.
Comparing the training accuracy, validation accuracy, training loss and validation loss of these five deep learning models, Densenet 121 model has shown best performance. Densenet 121 model achieved a recall and accuracy score of 99.86%. These findings demonstrate the immense scope of our method for actual use in the production of grape leaf, offering a cheaper and more feasible method for preventing disease and minimizing monetary harm.
Deep learning (DL), Detection and Classification, Grape leaf disease, Smart agriculture, Transfer learning
