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A lightweight real-time algorithm for plum harvesting detection in orchards under complex conditions

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Abstract

Accurate and efficient algorithms for detection are essential technologies utilized by robots for plum harvesting. To facilitate effective implementation on edge computing devices that have restricted processing capabilities, this research introduces a lightweight plum detection algorithm derived from YOLOv5s. Initially, the backbone network of the original architecture is substituted with the Revcol network. Subsequently, the feature space pyramid pooling (SPPF) is refined by integrating a Large Separable Kernel Attention (LSKA) mechanism, and the C3 module of the original model is improved through the use of Distributed Shift Convolutions (DSConv). Finally, a custom-designed lightweight detection head is employed to construct the lightweight plum detection model. The results show that the average mAP of the improved detection model is 96.3%, and the number of floating point operations (FLOPs) and parameters are reduced to 84.8% and 91.2% respectively. On edge computing devices, the inference speed reaches 44.64 frames per millisecond. This algorithm features high accuracy, lightweight design, and fast inference speed, providing valuable references for real-time object detection deployment of plum harvesting robots on edge computing devices.

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No datasets were generated or analysed during the current study.

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Acknowledgements

For me, writing a paper for the first time was a very memorable experience. I learned a lot about research and the skills of writing a paper from this experience. All these could not have been achieved without the careful guidance and help from my teachers and fellow students. I would like to thank them very much. First of all, I would like to thank my supervisor for giving me the right direction as well as enlightenment when I couldn't proceed with my thesis. It gave me the confidence to continue. At the same time my tutor gave me important advice at the end of my dissertation. Second, I would also like to express my sincere gratitude to my partners who provided me with language assistance as well as helped me proofread my essay while giving me constructive advice and for helping me structure my knowledge over the past year and a half. Finally, I am very grateful to my family for their support as well as understanding in my research path.

Funding

The funding was provided by National Natural Science Foundation of China (Grant Nos 32202147).

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Authors and Affiliations

  1. College of Computer and Control Engineering, The University of Northeast Forestry, Harbin, 150040, China

    Dongyan Zhang, Nuo Chen, Siyu Mao, Chenxv Wu, Dandan Gu & Lanxiang Zhang

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  1. Dongyan Zhang
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  2. Nuo Chen
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  3. Siyu Mao
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  4. Chenxv Wu
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  5. Dandan Gu
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Contributions

Z.D.Y. provided theoretical support, wrote the manuscript C.N. wrote the manuscript, processed the dataset, and prepared all graphs and tables M.S.Y. reviewed the manuscript, performed experiments W.C.X. reviewed the manuscript, checked the literature G.D.D. reviewed the manuscript, processed the dataset Z.L.X. reviewed the manuscript, checked grammar.

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Correspondence to Dongyan Zhang.

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Zhang, D., Chen, N., Mao, S. et al. A lightweight real-time algorithm for plum harvesting detection in orchards under complex conditions. SIViP 19, 327 (2025). https://doi.org/10.1007/s11760-025-03864-8

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  • DOI: https://doi.org/10.1007/s11760-025-03864-8

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