ArticlePlants (Basel, Switzerland)2025
ELD-YOLO: A Lightweight Framework for Detecting Occluded Mandarin Fruits in Plant Research.
Article in Plants (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Advances in Artificial Intelligence for Plant Research.Plants (Basel, Switzerland) · 2026Article
- An improved YOLOv11n-based method for high-precision detection of ginkgo fruits in complex natural environments.Frontiers in plant science · 2026Article
- Detection ofFrontiers in plant science · 2026Article
- A deep learning model for real-time recognition of immature persimmons in complex field scenarios.Frontiers in plant science · 2026Article
- OTB-YOLO: An Enhanced Lightweight YOLO Architecture for UAV-Based Maize Tassel Detection.Plants (Basel, Switzerland) · 2025Article
- A lightweight tri-modal few-shot detection framework for fruit diversity recognition toward digital orchard archiving.Frontiers in plant science · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
Mandarin fruit detection provides crucial technical support for yield prediction and the precise identification and harvesting of mandarin fruits. However, challenges such as occlusion from leaves or branches, the presence of small or partially visible fruits, and limitations in model efficiency pose significant obstacles in a complex orchard environment. To tackle these issues, we propose ELD-YOLO, a lightweight detection framework designed to enhance edge detail preservation and improve the detection of small and occluded fruits. Our method incorporates edge-aware processing to strengthen feature representation, introduces a streamlined detection head that balances accuracy with computational cost, and employs an adaptive upsampling strategy to minimize information loss during feature scaling. Experiments on a mandarin fruit dataset show that ELD-YOLO achieves a precision of 89.7%, a recall of 83.7%, an mAP@50 of 92.1%, and an mAP@50:95 of 68.6% while reducing the parameter count by 15.4% compared with the baseline. These results demonstrate that ELD-YOLO provides an effective and efficient solution for fruit detection in complex orchard scenarios.
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Registered trials
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