MLG-YOLO: A Model for Real-Time Accurate Detection and Localization of Winter Jujube in Complex Structured Orchard Environments.

IF 7.6 1区 农林科学 Q1 AGRONOMY Plant Phenomics Pub Date : 2024-09-23 eCollection Date: 2024-01-01 DOI:10.34133/plantphenomics.0258
Chenhao Yu, Xiaoyi Shi, Wenkai Luo, Junzhe Feng, Zhouzhou Zheng, Ayanori Yorozu, Yaohua Hu, Jiapan Guo
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引用次数: 0

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 X, Y, and Z directions are 4.20, 4.70, and 3.90 mm, respectively. This method enables accurate detection and localization of winter jujubes, providing technical support for winter jujube harvesting robots.

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MLG-YOLO:在结构复杂的果园环境中实时准确检测和定位冬枣的模型。
我们的研究重点是冬枣树,在温室环境下的结构化果园中进行,以有效控制各种生长条件。开发冬枣收获机器人系统对于实现机械化收获至关重要。高效收获冬枣需要准确的检测和定位。为解决这一问题,我们提出了一种基于 MobileVit-Large selective kernel-GSConv-YOLO (MLG-YOLO) 模型的冬枣检测和定位方法。首先,构建一个包含各种光照条件和叶片遮挡情况的冬枣数据集来训练模型。随后,提出了基于 YOLOv8n 的 MLG-YOLO 模型,并对其进行了改进,包括加入 MobileViT 来重建骨干网,使模型更加轻量级。利用 LSKblock 增强了颈部,以捕捉更广泛的上下文信息,并引入了轻量级卷积技术 GSConv,以进一步提高检测精度。最后,提出了一种结合 MLG-YOLO 和 RGB-D 相机的三维定位方法。通过在实验室环境和结构化果园环境中进行的烧蚀研究、对比实验、三维定位误差测试和全尺寸树木检测测试,证实了 MLG-YOLO 模型在检测和定位冬枣方面的有效性。与基准 YOLOv8n 模型相比,MLG-YOLO 的 mAP 增加了 3.50%,参数数量减少了 61.03%。与主流的物体检测模型相比,MLG-YOLO 在检测准确率和模型大小方面都表现出色,其 mAP 为 92.70%,准确率为 86.80%,召回率为 84.50%,模型大小仅为 2.52 MB。冬枣实验室环境测试的平均检测准确率达到 100%,结构化果园环境准确率达到 92.82%。X、Y和Z方向的绝对定位误差分别为4.20、4.70和3.90毫米。该方法实现了冬枣的精确检测和定位,为冬枣收获机器人提供了技术支持。
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来源期刊
Plant Phenomics
Plant Phenomics Multiple-
CiteScore
8.60
自引率
9.20%
发文量
26
审稿时长
14 weeks
期刊介绍: Plant Phenomics is an Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and published by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. The mission of Plant Phenomics is to publish novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics. The scope of the journal covers the latest technologies in plant phenotyping for data acquisition, data management, data interpretation, modeling, and their practical applications for crop cultivation, plant breeding, forestry, horticulture, ecology, and other plant-related domains.
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