Improved estimation of herbaceous crop aboveground biomass using UAV-derived crop height combined with vegetation indices-Reference-Cited by-同舟云学术

Improved estimation of herbaceous crop aboveground biomass using UAV-derived crop height combined with vegetation indices

Published:2022-10-05 Issue:2 Volume:24 Page:587-606
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Corti Martina^ORCID,Cavalli Daniele,Cabassi Giovanni,Bechini Luca,Pricca Nicolò,Paolo Dario,Marinoni Laura,Vigoni Antonio,Degano Luigi,Marino Gallina Pietro

Funder

Regione Lombardia

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences

Link

https://link.springer.com/content/pdf/10.1007/s11119-022-09960-w.pdf

Reference43 articles.

1. Azimi, S., Kaur, T., & Gandhi, T. K. (2021). A deep learning approach to measure stress level in plants due to nitrogen deficiency. Measurement, 173, 108650. https://doi.org/10.1016/j.measurement.2020.108650

2. Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6, 10395–10412. https://doi.org/10.3390/rs61110395

3. Bendig, J., Kang, Y., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M. L., & Bareth, G. (2015). Combining UAV-based plant height from crop surface models, visible and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87. https://doi.org/10.1016/j.jag.2015.02.012

4. Calou, V. B., Teixeira, A. D. S., Moreira, L. C., da Rocha Neto, O. C., & da Silva, J. A. (2019). Estimation of maize biomass using unmanned aerial vehicles. Engenharia Agrícola, 39, 744–752. https://doi.org/10.1590/1809-4430-eng.agric.v39n6p744-752/2019

5. Cao, H., Gu, X., Wei, X., Yu, T., & Zhang, H. (2020). Lookup table approach for radiometric calibration of miniaturized multispectral camera mounted on an unmanned aerial vehicle. Remote Sensing, 12, 4012. https://doi.org/10.3390/rs12244012

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction accuracy and repeatability of UAV based biomass estimation in wheat variety trials as affected by variable type, modelling strategy and sampling location;Plant Methods;2024-08-20

2. Estimating Bermudagrass Aboveground Biomass Using Stereovision and Vegetation Coverage;Remote Sensing;2024-07-19

3. Early sowing dates and pre-plant nitrogen affect autumn weed control and nitrogen content of winter cover crops in rotation with spring crops;European Journal of Agronomy;2024-04

4. Improving potato AGB estimation to mitigate phenological stage impacts through depth features from hyperspectral data;Computers and Electronics in Agriculture;2024-04

5. Inversion of winter wheat leaf area index from UAV multispectral images: classical vs. deep learning approaches;Frontiers in Plant Science;2024-03-14