AI-Driven Classification of Grape Plant Diseases from Leaf Images
Supervisor Name
Hadi Khalilia
Supervisor Email
h.khalilia@ptuk.edu.ps
University
Palestine Technical University Kadoorie
Research field
Computer Science
Bio
Hadi Khalilia is a faculty member in the field of Computer Science and Artificial Intelligence, with a strong interdisciplinary background spanning computational linguistics, natural language processing (NLP), information retrieval, and machine learning. He serves as Head of Computer Science at the College of Information Technology and Artificial Intelligence at Palestine Technical University (PTUK) and is a research group member of the KnowDive Research Group at the University of Trento, Italy. He holds a Ph.D. in Information and Communication Technology from the University of Trento, where his research focused on developing language resources and addressing lexical gaps in natural languages. His research interests include computational linguistics, natural language processing, language diversity, information retrieval, and machine learning. He has published extensively in international journals and conferences, including Frontiers in Psychology, LREC, ICNLSP, and ACL workshops. His work has contributed significantly to advancing multilingual lexicons, improving Arabic WordNet quality, and studying lexical diversity across languages and dialects. In addition, Dr. Khalilia has held several academic leadership and administrative roles.
Description
Early detection of grape leaf diseases is essential for reducing crop loss and enabling timely treatment decisions in vineyards. This project proposes an AI-based system for classifying grape leaf diseases from real-world images captured under field conditions. The system combines image preprocessing and transfer learning using a pre-trained convolutional neural network to accurately identify disease categories and suggest appropriate treatment plans. The model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score, and is designed to support real-time or mobile deployment for practical vineyard use.
