Year: 2025
Type: article
Abstract: In the conventional agricultural production process, the harvesting of mature fruits is frequently dependent on the observation and labor of workers, a process that is often time-consuming and labor-intensive. This study proposes an enhanced YOLOv7 detection and recognition model that incorporates a cross-spatial-channel 3D attention mechanism, a prediction head, and a weighted bidirectional feature pyramid neck optimization. The motivation for this study is to address the issues of uneven target distribution, mutual occlusion of fruits, and uneven light distribution that are prevalent in harvesting operations within orchards. The experimental findings demonstrate that the proposed model achieves an [email protected]–0.95 of 89.3%, representing an enhancement of 8.9% in comparison to the initial network. This method has resolved the issue of detecting and positioning the harvesting manipulator in complex orchard scenarios, thereby providing technical support for unmanned agricultural operations.
Source: Horticulturae
Authors: Jie Wu, Huawei Yang, Shucheng Wang, Ning Li, Xiaojie Shi, Xuzhen Lu, Zhimin Lun, Shaowei Wang, Supakorn Wongsuk, Peng Qi
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,654
OpenAlex ID: W7115911058
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