Fully automated proximal hyperspectral imaging system for high-resolution and high-quality in vivo soybean phenotyping
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Link
https://link.springer.com/content/pdf/10.1007/s11119-023-10045-5.pdf
Reference33 articles.
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3. Bradley, C. A., Allen, T. W., Sisson, A. J., Bergstrom, G. C., Bissonnette, K. M., Bond, J., Byamukama, E., Chilvers, M. I., Collins, A. A., Damicone, J. P., Dorrance, A. E., Dufault, N. S., Esker, P. D., Faske, T. R., Fiorellino, N. M., Giesler, L. J., Hartman, G. L., Hollier, C. A., Isakeit, T., & Wise, K. A. (2021). Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2015 to 2019. Plant Health Progress, 22(4), 483–495. https://doi.org/10.1094/PHP-01-21-0013-RS
4. Campbell, T. (2021). TSC07921.
5. Chen, Z., Wang, J., Wang, T., Song, Z., Li, Y., Huang, Y., Wang, L., & Jin, J. (2021). Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform. Computers and Electronics in Agriculture, 183, 105996. https://doi.org/10.1016/j.compag.2021.105996
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