#5919. Multi-level feature fusion for fruit bearing branch keypoint detection

August 2026publication date
Proposal available till 18-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Horticulture;
Agronomy and Crop Science;
Forestry;
Computer Science Applications;
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Abstract:
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.
Keywords:
Bearing branch pruning; Convolutional neural network; Fruit picking; Keypoint detection; Multi-level feature fusion

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