Benchmarking Faster R-CNN Backbones for Explainable Lemon Leaf Disease Detection
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Abstract
Accurate and timely detection of lemon leaf diseases is important for maintaining yield and fruit quality, yet manual scouting is labor intensive and subjective. This study explores a region-based deep learning method for symptom localization by benchmarking Faster R-CNN with various backbone feature extractors, including ResNet50, ResNet101, VGG16, and MobileNetV3, on a dataset of lemon leaf diseases annotated with bounding boxes. To ensure a fair comparison, all models are trained using identical preprocessing, augmentation, and optimization settings. They are evaluated based on detection metrics at multiple Intersection over Union (IoU) thresholds, which include mean Average Precision (mAP) at 0.5 to 0.95, mAP at 0.50, mAP at 0.75, average recall, and mean IoU. Soft Non-Maximum Suppression is also applied during inference to reduce missed detections when lesions appear close together in clusters. Results indicate that Faster R-CNN with a ResNet50 backbone and Soft Non-Maximum Suppression provides the best overall performance, achieving an mAP from 0.5 to 0.95 of 0.6584 and an mAP at 0.75 of 0.8052, while maintaining a strong recall of 0.7326 and high localization quality with a mean IoU of 0.8203. To ensure trustworthy deployment, model explanations are generated using Grad-CAM and LIME. These methods demonstrate that the detector primarily focuses on symptomatic regions instead of background patterns. Rather than proposing a new detector architecture, this work provides a controlled benchmark of backbone selection under a unified Faster R-CNN pipeline. The proposed pipeline presents an effective and interpretable solution for monitoring lemon leaf disease within the evaluated dataset and experimental setting. It also offers practical guidance for selecting backbones in region-based agricultural inspection systems.
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