Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots
Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. B...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/145 |
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| _version_ | 1832589385383542784 |
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| author | Xiaojie Shi Shaowei Wang Bo Zhang Xinbing Ding Peng Qi Huixing Qu Ning Li Jie Wu Huawei Yang |
| author_facet | Xiaojie Shi Shaowei Wang Bo Zhang Xinbing Ding Peng Qi Huixing Qu Ning Li Jie Wu Huawei Yang |
| author_sort | Xiaojie Shi |
| collection | DOAJ |
| description | Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on. |
| format | Article |
| id | doaj-art-29b07c76913344638b90bf3e33888661 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-29b07c76913344638b90bf3e338886612025-01-24T13:16:55ZengMDPI AGAgronomy2073-43952025-01-0115114510.3390/agronomy15010145Advances in Object Detection and Localization Techniques for Fruit Harvesting RobotsXiaojie Shi0Shaowei Wang1Bo Zhang2Xinbing Ding3Peng Qi4Huixing Qu5Ning Li6Jie Wu7Huawei Yang8Shandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaShandong Academy of Agricultural Machinery Sciences, Jinan 250100, ChinaDue to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on.https://www.mdpi.com/2073-4395/15/1/145object detectionobject localizationdeep learningcomputer visionfruit-harvesting robots |
| spellingShingle | Xiaojie Shi Shaowei Wang Bo Zhang Xinbing Ding Peng Qi Huixing Qu Ning Li Jie Wu Huawei Yang Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots Agronomy object detection object localization deep learning computer vision fruit-harvesting robots |
| title | Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots |
| title_full | Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots |
| title_fullStr | Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots |
| title_full_unstemmed | Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots |
| title_short | Advances in Object Detection and Localization Techniques for Fruit Harvesting Robots |
| title_sort | advances in object detection and localization techniques for fruit harvesting robots |
| topic | object detection object localization deep learning computer vision fruit-harvesting robots |
| url | https://www.mdpi.com/2073-4395/15/1/145 |
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