A Deep Learning-Based Intelligent Approach for Automated Olive Leaf Disease Detection Using the Optimized VGG16
Received: 1 November 2025 | Revised: 4 December 2025 and 16 December 2025 | Accepted: 18 December 2025 | Online: 9 February 2026
Corresponding author: Mohamed S. Sawah
Abstract
Olive leaf diseases significantly impact crop yield and quality, posing a threat to agricultural productivity and economic stability. Traditional disease detection methods are often time-consuming and costly. This study presents an automated deep learning approach for olive leaf disease classification, utilizing the VGG16 architecture with optimized fine-tuning. A dataset of 954 olive leaf images (382 infected, 572 healthy) collected from agricultural fields in Turkey was augmented via rotation, cropping, and flipping to enhance model robustness. The pre-trained VGG16 model was fine-tuned for feature extraction, followed by classification layers incorporating dropout and Softmax activation. Experimental results demonstrated a state-of-the-art classification accuracy of 99.47%, outperforming existing methods. The results confirm the effectiveness of deep learning for agricultural automation, providing a rapid, low-cost solution for early disease detection and management.
Keywords:
olive leaf diseases, image classification, machine learning, agricultural automation, transfer learningDownloads
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Copyright (c) 2025 Issam Trrad, Issa Alsmadi, Mohamed S. Sawah
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