Simultaneously predicting <scp>SPAD</scp> and water content in rice leaves using hyperspectral imaging with deep multi‐task regression and transfer component analysis
Compared with the original model, good and differentiated results were obtained for the models using features learned by TCA for both the source domain and target domain. The multi-task models could be constructed to predict SPAD values and water content simultaneously and then transferred to another rice variety, which could improve the efficiency of model construction and realize rapid detection of rice growth indicators. © 2024 Society of Chemical Industry.
Authors
- Lei Zhou (ORCID: https://orcid.org/0000-0001-5857-8153)
- Yun Ren (ORCID: https://orcid.org/0000-0003-2171-5913)
- Xincheng Zhang (ORCID: https://orcid.org/0000-0002-8182-7178)
- Chu Zhang (ORCID: https://orcid.org/0000-0001-6760-3154)
- Yuanning Zhai
- Jun Wang (ORCID: https://orcid.org/0009-0008-8475-2934)
- Hengnian Qi (ORCID: https://orcid.org/0000-0002-1927-7160)
Institutions
- Nanjing Forestry University (CN)
- Huzhou Normal University (CN)
- Huzhou Academy of Agricultural Sciences (CN)
Publication Details
- Journal
- Journal of the Science of Food and Agriculture
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1002/jsfa.13853
- Citations
- 5
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 1.18
Funders
- National Natural Science Foundation of China