Design and Testing of a Seedling Pick-Up Device for a Facility Tomato Automatic Transplanting Machine DOI Creative Commons

Zhicheng Liu,

Lu Shi, Zhiyuan Liu

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6700 - 6700

Published: Oct. 18, 2024

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency operation, this paper designs a clip stem automatic seedling picking device based on yolov5 algorithm. First all, through study characteristics seedlings different ages, age suitable for was obtained. Secondly, improved algorithm used determine position shape seedlings. By adding lightweight upsampling operator (CARAFE) loss function, feature extraction ability detection speed stems were improved. The accuracy reached 92.6%, mAP_0.5 95.4%. Finally, verification test carried out with about 40 days old. results show that damage rate 7.2%, success not less than 90.3%. This can provide reference research into machines.

Language: Английский

Design and Testing of a Seedling Pick-Up Device for a Facility Tomato Automatic Transplanting Machine DOI Creative Commons

Zhicheng Liu,

Lu Shi, Zhiyuan Liu

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(20), P. 6700 - 6700

Published: Oct. 18, 2024

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency operation, this paper designs a clip stem automatic seedling picking device based on yolov5 algorithm. First all, through study characteristics seedlings different ages, age suitable for was obtained. Secondly, improved algorithm used determine position shape seedlings. By adding lightweight upsampling operator (CARAFE) loss function, feature extraction ability detection speed stems were improved. The accuracy reached 92.6%, mAP_0.5 95.4%. Finally, verification test carried out with about 40 days old. results show that damage rate 7.2%, success not less than 90.3%. This can provide reference research into machines.

Language: Английский

Citations

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