Maize plant height automatic reading of measurement scale based on improved YOLOv5 lightweight model
The improved model achieved an average precision of 98.6%, a computational complexity of 1.2 GFLOPs, and occupied 1.8 MB of memory. The detection frame rate on the computer was 54.1 fps. Through comparisons with models such as YOLOv5s, YOLOv7 and YOLOv8s, it was evident that the comprehensive performance of the improved model in this study was superior. Finally, a comparison between the algorithm's 160 plant height data obtained from the test set and manual readings demonstrated that the relative error between the algorithm's results and manual readings was within 0.2 cm, meeting the requirements of automatic reading of maize height measuring scale.
Authors
- Yanan Zhou (ORCID: https://orcid.org/0000-0003-2225-3001)
- Dayu Pan (ORCID: https://orcid.org/0000-0003-0284-9350)
- Ying Gu (ORCID: https://orcid.org/0000-0003-3503-6436)
- Jiachao Li
- He Zhang (ORCID: https://orcid.org/0000-0003-3782-1411)
- Bin Luo (ORCID: https://orcid.org/0000-0001-7007-2489)
Institutions
- Xinjiang Agricultural University (CN)
- Northeast Agricultural University (CN)
- National Engineering Research Center for Information Technology in Agriculture (CN)
Publication Details
- Journal
- PeerJ Computer Science
- Published
- 2024-08-05
- DOI
- https://doi.org/10.7717/peerj-cs.2207
- Citations
- 2
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.20
Funders
- National Natural Science Foundation of China