Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach
In this study, we successfully developed a method for the non-destructive detection of the early stages of eggplant wilt disease by combining multispectral imaging technology with deep learning algorithms. While ensuring high accuracy, this method significantly reduces the cost of experimental equipment. The application of this method can reduce the cost of agricultural equipment and provide a scientific basis for agricultural production practices, helping to reduce losses caused by diseases.
Authors
- Youwei Zhang (ORCID: https://orcid.org/0000-0001-7953-5929)
- Xiaofei Fan (ORCID: https://orcid.org/0009-0008-6329-9616)
- Feixue Cheng (ORCID: https://orcid.org/0000-0002-8865-1072)
- Dongfang Zhang (ORCID: https://orcid.org/0000-0002-7017-0502)
- Yunfei Zhang (ORCID: https://orcid.org/0000-0001-8160-7995)
- Wang Min
- Xinqing Zhao (ORCID: https://orcid.org/0000-0001-7901-1463)
Institutions
- Hebei Agricultural University (CN)
- Center for Agricultural Resources Research (CN)
Publication Details
- Journal
- Plant Methods
- Published
- 2024-11-15
- DOI
- https://doi.org/10.1186/s13007-024-01291-3
- Citations
- 11
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 6.60
Funders
- Innovative Research Group Project of the National Natural Science Foundation of China
- National Natural Science Foundation of China
- China Agricultural Research System