ArticlePlant phenomics (Washington, D.C.)2024
MLG-YOLO: A Model for Real-Time Accurate Detection and Localization of Winter Jujube in Complex Structured Orchard Environments.
Article in Plant phenomics (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Species-specific tree structural parameters extraction via UAV RGB-LiDAR data and multimodal instance segmentation.Plant phenomics (Washington, D.C.) · 2026Article
- BR-YOLOv9: a multi-scale fusion framework for robust weed recognition in rice paddy fields.Frontiers in plant science · 2026Article
- Depth Imaging-Based Framework for Efficient Phenotypic Recognition in Tomato Fruit.Plants (Basel, Switzerland) · 2025Article
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8 authors.
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Abstract
Our research focuses on winter jujube trees and is conducted in a greenhouse environment in a structured orchard to effectively control various growth conditions. The development of a robotic system for winter jujube harvesting is crucial for achieving mechanized harvesting. Harvesting winter jujubes efficiently requires accurate detection and location. To address this issue, we proposed a winter jujube detection and localization method based on the MobileVit-Large selective kernel-GSConv-YOLO (MLG-YOLO) model. First, a winter jujube dataset is constructed to comprise various scenarios of lighting conditions and leaf obstructions to train the model. Subsequently, the MLG-YOLO model based on YOLOv8n is proposed, with improvements including the incorporation of MobileViT to reconstruct the backbone and keep the model more lightweight. The neck is enhanced with LSKblock to capture broader contextual information, and the lightweight convolutional technology GSConv is introduced to further improve the detection accuracy. Finally, a 3-dimensional localization method combining MLG-YOLO with RGB-D cameras is proposed. Through ablation studies, comparative experiments, 3-dimensional localization error tests, and full-scale tree detection tests in laboratory environments and structured orchard environments, the effectiveness of the MLG-YOLO model in detecting and locating winter jujubes is confirmed. With MLG-YOLO, the mAP increases by 3.50%, while the number of parameters is reduced by 61.03% in comparison with the baseline YOLOv8n model. Compared with mainstream object detection models, MLG-YOLO excels in both detection accuracy and model size, with a mAP of 92.70%, a precision of 86.80%, a recall of 84.50%, and a model size of only 2.52 MB. The average detection accuracy in the laboratory environmental testing of winter jujube reached 100%, and the structured orchard environmental accuracy reached 92.82%. The absolute positioning errors in the
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