A Computer Vision and AI-Based System for Real-Time Detection and Diagnosis of Olive Leaf Diseases

This paper introduces OLIVE-CAD, a novel Computer-Aided Diagnostics system designed for the real life, on-site detection of olive leaf diseases. The core of the system is a YOLOv12-based convolutional neural network model, which was trained on a comprehensive dataset of 11,315 olive leaf images. The images were categorized into 'Aculus', 'Scab', and 'Healthy,' with the dataset divided for training (70%), evaluation (20%), and real-world testing (10%). The key contribution of this work is the end-to-end integration of a custom, field-deployable Computer-Aided Diagnostics system. The trained YOLOv12 model achieved a mean average precision of 98.2% and mean average recall of 95.4%, while the model achieves class-specific evaluation precision of 95.3% and recall of 97.7% for 'Healthy' class; 97.9% precision and 88.3% of recall for 'Aculus' class; and precision of 94.3% and 95.4% of recall for 'Scab' class. OLIVE-CAD enables the storage of the immediate disease diagnostic outcomes to a predesigned database, providing a practical, deployable solution for agricultural applications. The research recommends an IoT-Based real-time central operation diagnostic monitoring system as future work.