Modular autonomous strawberry picking robotic system

被引:12
作者
Parsa, Soran [1 ]
Debnath, Bappaditya [2 ]
Khan, Muhammad Arshad [1 ]
Ghalamzan, Amir E. [1 ]
机构
[1] Univ Lincoln, Lincoln Inst Agrifood Technol, Lincoln, England
[2] Kings Coll London, London, England
关键词
agricultural robotics; computer vision; motion planning; precision farming; robotic manipulation; selective harvesting; HARVESTING ROBOT; FIELD-EVALUATION; DESIGN; LOCALIZATION; RECOGNITION;
D O I
10.1002/rob.22229
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the selective harvesting of strawberries is a complicated robotic task forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, for example, picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g., high-yielding and/or disease-resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, for example, dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5-degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localize strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new data sets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section.
引用
收藏
页码:2226 / 2246
页数:21
相关论文
共 67 条
  • [61] Development of a Stationary Robotic Strawberry Harvester with a Picking Mechanism that Approaches the Target Fruit from Below
    Yamamoto, Satoshi
    Hayashi, Shigehiko
    Yoshida, Hirotaka
    Kobayashi, Ken
    [J]. JARQ-JAPAN AGRICULTURAL RESEARCH QUARTERLY, 2014, 48 (03): : 261 - 269
  • [62] Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN
    Yu, Yang
    Zhang, Kailiang
    Yang, Li
    Zhang, Dongxing
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 163
  • [63] Detection and classification of bruises of pears based on thermal images
    Zeng, Xiangyu
    Miao, Yu
    Ubaid, Saima
    Gao, Xiumin
    Zhuang, Songlin
    [J]. POSTHARVEST BIOLOGY AND TECHNOLOGY, 2020, 161
  • [64] Deep Learning Based Improved Classification System for Designing Tomato Harvesting Robot
    Zhang, Li
    Jia, Jingdun
    Gui, Guan
    Ha, Xia
    Gao, Wanlin
    Wang, Minjuan
    [J]. IEEE ACCESS, 2018, 6 : 67940 - 67950
  • [65] Zhang ZP, 2014, LECT NOTES COMPUT SC, V8694, P94, DOI 10.1007/978-3-319-10599-4_7
  • [66] Dual-arm Robot Design and Testing for Harvesting Tomato in Greenhouse
    Zhao, Yuanshen
    Gong, Liang
    Liu, Chengliang
    Huang, Yixiang
    [J]. IFAC PAPERSONLINE, 2016, 49 (16): : 161 - 165
  • [67] Computer vision-based localisation of picking points for automatic litchi harvesting applications towards natural scenarios
    Zhuang, Jiajun
    Hou, Chaojun
    Tang, Yu
    He, Yong
    Guo, Qiwei
    Zhong, Zhenyu
    Luo, Shaoming
    [J]. BIOSYSTEMS ENGINEERING, 2019, 187 : 1 - 20