Quantifying corn LAI using machine learning and UAV multispectral imaging-Reference-Cited by-同舟云学术

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Quantifying corn LAI using machine learning and UAV multispectral imaging

Published:2024-03-28 Issue:4 Volume:25 Page:1777-1799
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Cheng Qian,Ding Fan,Xu Honggang,Guo Shuzhe,Li Zongpeng,Chen Zhen^ORCID

Funder

Technology integration and demonstration application of intelligent sprinkler irrigation system in Huanghuai area

Central Public-interest Scientific Institution Basal Research Fund

Key Grant Technology Project of Henan

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11119-024-10134-z.pdf

Reference64 articles.

1. Bajocco, S., Ginaldi, F., Savian, F., Morelli, D., Scaglione, M., Fanchini, D., Raparelli, E., & Bregaglio, S. U. M. (2022). On the use of NDVI to estimate LAI in field crops: Implementing a conversion equation library. Remote Sensing, 14, 14. https://doi.org/10.3390/rs14153554

2. Basal, O., & Szabó, A. (2020). Physiomorphology of soybean as affected by drought stress and nitrogen application. Scientifica, 2020, 7. https://doi.org/10.1155/2020/6093836

3. Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2022). Deep learning techniques to classify agricultural crops through UAV imagery: A review. Neural Computing & Applications, 34, 9511–9536. https://doi.org/10.1007/s00521-022-07104-9

4. Broge, N. H., & Mortensen, J. V. (2002). Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Sensing of Environment, 81, 45–57. https://doi.org/10.1016/s0034-4257(01)00332-7

5. Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7, 4026–4047. https://doi.org/10.3390/rs70404026

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