文献类型： 外文期刊
作者： Sun, Qixin 1 ; Chai, Xiujuan 1 ; Zeng, Zhikang 3 ; Zhou, Guomin 1 ; Sun, Tan 1 ;
作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China
2.Minist Agr & Rural Affairs, Key Lab Agr Big Data, Beijing 100081, Peoples R China
3.Guangxi Acad Agr Sci, Agr Sci & Technol Informat Res Inst, Nanning 530007, Peoples R China
关键词： Fruit picking; Bearing branch pruning; Convolutional neural network; Keypoint detection; Multi-level feature fusion
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）
ISSN： 0168-1699
年卷期： 2021 年 191 卷
页码：
收录情况： SCI
摘要： Automated orchard operation has been a firm goal of fruit farmers for a long time. Deep learning-based approaches have been widely used to improve the performance of fruit detection, branch pruning, production estimating and other agricultural operations. This paper proposes a novel method to detect keypoint on the branch, which enables branch pruning during fruit picking. Specifically, a top-down framework for bearing branch keypoint detection is developed. First, a candidate area is generated according to the fruit-growing position and the fruit stem keypoint detection, which provides an attention region for further keypoint detection. Second, a multi-level feature fusion network which combines features in the same spatial sizes (intra-level) and from different spatial sizes (inter-level) is proposed to detect keypoint within the candidate area. The network can learn the spatial and semantic information and model the relationship among bearing branch keypoints. In addition, this paper constructs a citrus bearing branch dataset, which contributes to comprehensively evaluating the proposed method. Evaluation metrics on the dataset indicate the proposed method reaches an AP of 77.4% and an accuracy score of 84.7% with smaller model size and lower computing power consumption, which significantly outperforms several state-of-the-art keypoint detection methods. This study provides the possibility and foundation for performing automatic branch pruning during fruit harvesting.
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