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Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: a review

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Abstract

The demand for intelligent agriculture is increasing due to the continuous impact of world food and environmental crises. Focusing on fruit detection, with the rapid development of object detection technology, it is now possible to achieve high efficiency and high accuracy in fruit detection systems. However, detecting fruit with high precision in unstructured orchard environments remains particularly challenging. Such environments, which are composed of varying illumination conditions and degrees of occlusion, can be mitigated by certain strategies. To our knowledge, this is the first time that optimization strategies used in fruit detection have been reviewed. This review aims to explore methods for improving fruit detection in complex environments. First, we describe the common types of complex backgrounds found in outdoor orchard environments. Subsequently, we divide the improvement measures into two categories: optimization before and after image sampling. Next, we compare the test results obtained before and after the application of these improved methods. Finally, we describe the future development trends of fruit detection optimization technology in complex backgrounds. We hope that this review will inspire researchers to design their optimization strategies and help explore lower-cost and more robust fruit detection systems.

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Acknowledgements

This work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515140162, 2022A1515140013), the Key-area Research and Development Program of Guangdong Province (2019B020223003), Science and Technology Planning Project of Guangzhou (202102080269), the Natural Science Foundation of China (32171909).

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  1. College of Urban and Rural Construction, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China

    Yunchao Tang

  2. College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China

    Jiajun Qiu, Yunqi Zhang, Dongxiao Wu, Yuhong Cao & Lixue Zhu

  3. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510000, China

    Kexin Zhao

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YT: Conceptualization, Reviewing, Editing. JQ: Conceptualization, Writing—original draft. YZ: Chart drawing and beautification. DW: Investigation. YC: Chart beautification. KZ: Editing. LZ: Supervision.

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Tang, Y., Qiu, J., Zhang, Y. et al. Optimization strategies of fruit detection to overcome the challenge of unstructured background in field orchard environment: a review. Precision Agric 24, 1183–1219 (2023). https://doi.org/10.1007/s11119-023-10009-9

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