Proxi-detection to monitor the growth status of wheat in the presence of weeds using low-cost and simple digital tools to track the emergence of stress-Reference-Cited by-同舟云学术

Proxi-detection to monitor the growth status of wheat in the presence of weeds using low-cost and simple digital tools to track the emergence of stress

Published:2022-10-18 Issue:6 Volume:23 Page:2115-2134
ISSN:1385-2256
Container-title:Precision Agriculture
language:en
Short-container-title:Precision Agric

Author:

Gée Christelle^ORCID,Dujourdy Laurence^ORCID,Mignon Victor,Denimal Emmanuel^ORCID

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences

Link

https://link.springer.com/content/pdf/10.1007/s11119-022-09963-7.pdf

Reference49 articles.

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2. Bah, M. D., Hafane, A., & Canals, R. (2018). Deep Learning with unsupervised data labelling for weeds detection on UAV images. Remote Sensing, 10(11), 1690. https://doi.org/10.3390/rs10111690

3. Barnard, H. R., Findley, M. C., & Csavina, J. (2014). A simple and inexpensive device for logging photosynthetically active radiation. Tree Physiology, 34, 640–645. https://doi.org/10.1093/treephys/tpu044

4. Beillouin, D., Leclère, M., Barbu, C. M., Bénézit, M., Trépos, R., Gauffreteau, A., & Jeuffroy, M.-H. (2018). Azodyn-Barley, a winter-barley crop model for predicting and ranking genotypic yield, grain protein and grain size in contrasting pedoclimatic conditions. Agricultural and Forest Meteorology, 262, 237–248. https://doi.org/10.1016/j.agrformet.2018.06.002

5. Berge, T. W., Goldberg, S., Kaspersen, K., & Netland, J. (2012). Towards machine vision based site-specific weed management in cereals. Computers and Electronics in Agriculture, 81, 79–86. https://doi.org/10.1016/j.compag.2011.11.004

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