TY - JOUR
T1 - Design and Experiment of Nighttime Greenhouse Tomato Harvesting Robot
AU - Liu, Lei
AU - Yang, Qizhi
AU - He, Wenbing
AU - Yang, Xinyu
AU - Zhou, Qin
AU - Addy, Min Min
N1 - Publisher Copyright:
© 2024 Published by IRCS-ITB.
PY - 2024/6/19
Y1 - 2024/6/19
N2 - 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.
AB - 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.
KW - YOLOv5+HSV
KW - image denoising
KW - nighttime harvesting
KW - tomato harvesting robot
KW - visual recognition
UR - https://www.scopus.com/pages/publications/85199651979
UR - https://www.scopus.com/pages/publications/85199651979#tab=citedBy
U2 - 10.5614/j.eng.technol.sci.2024.56.3.3
DO - 10.5614/j.eng.technol.sci.2024.56.3.3
M3 - Article
AN - SCOPUS:85199651979
SN - 2337-5779
VL - 56
SP - 340
EP - 352
JO - Journal of Engineering and Technological Sciences
JF - Journal of Engineering and Technological Sciences
IS - 3
ER -