Fine-Tuned Transfer Learning with InceptionV3 for Automated Detection of Grapevine Leaf Diseases

Miftahus  Sholihin; Moh. Rosidi  Zamroni; Lilik Anifah; Mohd Farhan Md  Fudzee; Mohd Norasri  Ismail

doi:10.52436/1.jutif.2025.6.5.4717

Authors

Miftahus Sholihin Informatic Engineering, Universitas Islam Lamongan, Indonesia
Moh. Rosidi Zamroni Informatic Engineering, Universitas Islam Lamongan, Indonesia
Lilik Anifah Electrical Engineering Department, Universitas Negeri Surabaya, Indonesia
Mohd Farhan Md Fudzee Faculty of Computer Sciences and Information Technology, University Tun Hussein Onn Malaysia, Malaysia
Mohd Norasri Ismail Faculty of Computer Sciences and Information Technology, University Tun Hussein Onn Malaysia, Malaysia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.5.4717

Keywords:

Agriculture, Deep learning, Grape leaf disease, InceptionV3, Transfer learning.

Abstract

Grape leaf diseases pose a major threat to vineyard productivity, making early and accurate detection essential for modern grape plantation management. Despite advancements in computer vision, challenges remain in differentiating diseases with visually similar symptoms. This study addresses that gap by developing a grape leaf disease classification system using a fine-tuned deep learning model based on the InceptionV3 architecture. Three training scenarios were conducted with fixed parameters batch size of 32 and learning rate of 0.001while varying the number of epochs (25, 50, and 75). Results showed a consistent improvement in classification accuracy with increased training epochs, reaching 98.64%, 98.78%, and 99.09% respectively. Confusion matrix analysis revealed that most misclassifications occurred between visually similar diseases such as Black Rot and ESCA, but error rates declined as the number of epochs increased. Rather than merely applying transfer learning, this research highlights the impact of systematic tuning specifically epoch count optimization in enhancing model accuracy for difficult to distinguish disease classes. These findings underscore the urgency of developing high performance, automated disease detection tools to support precision agriculture and sustainable crop health monitoring.

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