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A method for multi-target segmentation of bud-stage apple trees based on improved YOLOv8

文献类型： 外文期刊

作者： Chen, Jincheng 1 ; Ji, Chao 2 ; Zhang, Jing 2 ; Feng, Qingchun 4 ; Li, Yujie 1 ; Ma, Benxue 1 ;

作者机构： 1.Shihezi Univ, Coll Mech & Elect Engn, Shihezi 832003, Peoples R China

2.Xinjiang Acad Agr & Reclamat Sci, Mech Equipment Res Inst, Shihezi 832000, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Northwest Agr Equipment, Shihezi 832003, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

5.Minist Educ Prod Mechanizat Oasis Characterist Eco, Engn Res Ctr, Shihezi 832003, Peoples R China

关键词： YOLOv8; Bud stage; Apple tree; Multi-target segmentation; Complex background

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 220 卷

页码：

收录情况： SCI

摘要： The bud stage is a crucial period in the growth and development of apple trees. Accurate detections of the physiological changes in the organs (branches, buds, leaves, and the connecting parts between buds and branches) are essential for the scientific management of orchards. In the field of intelligent orchard management, image segmentation is a fundamental method for obtaining the phenotypes of fruit tree organs, making it especially critical. To address this, we have created a dataset for apple tree organ segmentation during the bud stage and have incorporated several advanced convolutional network modules (ConvNeXt V2, Multi-scale Extended Attention Module (MSDA), Dynamic Snake Convolution (DSConv)) to enhance YOLOv8 and improve the accuracy of organ segmentation in complex natural environments. In the backbone network, we have integrated ConvNeXt V2 and MSDA modules to increase the extraction of contextual information and improve the network ' s ability to recognize multi-scale and multi-shaped targets. Additionally, we have embedded DSConv in the network ' s head and utilized deformable convolution to enable adaptive sampling of the feature map, capturing a wider range of context information and improving the local feature processing capability for objects of varying sizes, ultimately leading to improved segmentation accuracy. Our models significantly outperform existing models, achieving 82.58 % mean Precision (mP), 74.58 % mean Recall (mR), 77.94 % mean Dice (mDice), 64.91 % mean IoU(mIoU), and 79.75 % mean Average Precision (mAP). Ablation studies confirm the contributions of each module to intelligent orchard management and suggest potential benefits for precise agricultural decision-making and operations.

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