Design and Experiment of Nighttime Greenhouse Tomato Harvesting Robot

https://doi.org/10.5614/j.eng.technol.sci.2024.56.3.3
image denoising nighttime harvesting tomato harvesting robot visual recognition YOLOv5 HSV

Authors

  • Lei Liu School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China, China
  • Qizhi Yang
    yangqz@ujs.edu.cn
    Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China, China
  • Wenbing He School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China, China
  • Xinyu Yang Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China, China
  • Qin Zhou School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China, China
  • Min Min Addy Bioproducts and Biosystems Engineering Department, University of Minnesota, USA, 1731 Arona Street, Falcon Heights, MN 55113, United States , United States
Vol. 56 No. 3 (2024)
Articles
June 19, 2024

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In response to the issue of high tomato yield, low efficiency in harvesting tomatoes grown in greenhouses, and low recognition accuracy of nighttime harvesting robots, a design was developed and a robotic system was created specifically for nighttime greenhouse tomato harvesting. The robot employs a vision system and YOLOv5+HSV fusion algorithm to recognize and locate tomatoes. It then transmits this information to the robotic arm. By coordinating the visual system, the robotic arm, the end effector, and the lifting mechanism, the robot accurately picks ripe tomatoes. The robot was subjected to simulated field tests for visual recognition and harvesting, both during daytime and nighttime conditions. The results showed that the success rate of nighttime harvesting was slightly lower than during the daytime but remained at a relatively high level. The daytime harvesting success rate and the average time to pick a single fruit were 87.78% and 15.99 seconds, respectively. The nighttime harvesting success rate and the average time to pick a single fruit were 87.55% and 17.26 seconds, respectively. This approach effectively improves the recognition accuracy and harvesting speed of the harvesting robot, reducing damage to tomatoes during harvesting, and addresses the issues of supplementary lighting and image noise reduction for nighttime harvesting robots.