An improved model based on YOLOX for detection of tea sprouts in natural environment-Reference-Cited by-同舟云学术

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An improved model based on YOLOX for detection of tea sprouts in natural environment

Published:2024-05-08 Issue:5 Volume:15 Page:1665-1679
ISSN:1868-6478
Container-title:Evolving Systems
language:en
Short-container-title:Evolving Systems

Author:

Li Xiutong,Liu Ruixin,Li Yuxin,Li Zhilin,Yan Peng,Yu Mei,Dong Xuan,Yan Jianwei,Xie Benliang

Funder

National Key Research and Development Program

Guizhou Provincial Basic Research Program

Open Fund Project in Semiconductor Power Device Reliability Engineering Center of Ministry of Education

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12530-024-09589-2.pdf

Reference32 articles.

1. Cao ML, Fu H, Zhu JY, Cai CG (2022) Lightweight tea bud recognition network integrating GhostNet and YOLOv5. Math Biosci Eng MBE 19(12):12897–12914. https://doi.org/10.3934/mbe.2022602

2. Chen B, Yan JL, Wang K, Matušů R (2021) Fresh tea sprouts detection via image enhancement and fusion SSD. J Control Sci Eng 2021:1–11. https://doi.org/10.1155/2021/6614672

3. Chen CL, Lu JZ, Zhou MC, Yi J, Liao M, Gao ZM (2022) A YOLOv3-based computer vision system for identification of tea buds and the picking point. Comput Electron Agric 198:107116. https://doi.org/10.1016/j.compag.2022.107116

4. Cortes C, Vapnik V (1995) Support-vector networks. J Mach Ing 20:273–297. https://doi.org/10.1007/BF00994018

5. Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. Preprint https://doi.org/10.48550/arXiv.1810.04805

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