Weed identification in broomcorn millet field using segformer semantic segmentation based on multiple loss functions-Reference-Cited by-同舟云学术

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Weed identification in broomcorn millet field using segformer semantic segmentation based on multiple loss functions

Published:2024 Issue:1 Volume:17 Page:27-36
ISSN:1881-8366
Container-title:Engineering in Agriculture, Environment and Food
language:en
Short-container-title:Engineering in Agriculture, Environment and Food

Author:

BI Zhifang¹,LI Yanwen²,GUAN Jiaxiong²,LI Juxia²,ZHANG Pengpeng²,ZHANG Xiaoying³,HAN Yuanhuai⁴,WANG Linjuan¹,GUO Wenfeng¹

Affiliation:

1. Department of Basic Sciences, Shanxi Agricultural University

2. College of Information Science and Engineering, Shanxi Agricultural University

3. School of Software, Shanxi Agricultural University

4. College of Agriculture, Shanxi Agricultural University

Publisher

Asian Agricultural and Biological Engineering Association

Link

https://www.jstage.jst.go.jp/article/eaef/17/1/17_27/_pdf

Reference43 articles.

1. Abdalla, A. et al. 2019. Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure. Computers and Electronics in Agriculture. 167: 105091. https://doi.org/10.1016/j.compag.2019.105091

2. Asad, M. H. et al. 2020. Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture. 7 (4): 535–545. https://doi.org/10.1016/j.inpa.2019.12.002

3. Bakhshipour, A. et al. 2017. Weed segmentation using texture features extracted from wavelet sub-images. Biosystems Engineering. 157: 1–12. https://doi.org/10.1016/j.biosystemseng.2017.02.002

4. Berge, T. W. et al. 2012. Towards machine vision based site-specific weed management in cereals. Computers and Electronics in Agriculture. 81: 79–86. https://doi.org/10.1016/j.compag.2011.11.004

5. Bosilj, P. et al. 2020. Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture. Journal of Field Robotics. 37 (1): 7–19. https://doi.org/10.1002/rob.21869

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