Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.

Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of...

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Autores principales:	Ephrem Habyarimana, Faheem S Baloch
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cd3633b59c6e43f18b8c3b1fe1adf9f5
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spelling	oai:doaj.org-article:cd3633b59c6e43f18b8c3b1fe1adf9f52021-11-25T06:19:23ZMachine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.1932-620310.1371/journal.pone.0249136https://doaj.org/article/cd3633b59c6e43f18b8c3b1fe1adf9f52021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0249136https://doaj.org/toc/1932-6203Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of absorbed photosynthetically active radiation (fAPAR), chlorophyll concentration (CI) and normalized difference vegetation (NDVI) indices were used in crop monitoring, but their comparative performances in sorghum monitoring is lacking. This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance. Bayesian ridge regression showed good cross-validated performance, and high reliability (R2 = 35%) and low bias (mean absolute prediction error, MAPE = 0.4%) during the validation step. Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to Sentinel-2's fAPAR. The best times to sample the vegetation indices were the months of June (second half) and July. The results obtained in this work will serve several purposes including improvements in plant breeding, farming management and sorghum biomass yield forecasting at extension services and policy making levels.Ephrem HabyarimanaFaheem S BalochPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 3, p e0249136 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ephrem Habyarimana Faheem S Baloch Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
description	Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of absorbed photosynthetically active radiation (fAPAR), chlorophyll concentration (CI) and normalized difference vegetation (NDVI) indices were used in crop monitoring, but their comparative performances in sorghum monitoring is lacking. This work aimed therefore at closing this gap by evaluating the performance of machine learning modelling of in-season sorghum biomass yields based on Sentinel-2-derived fAPAR and simpler high-throughput optical handheld meters-derived NDVI and CI calculated from sorghum plants reflectance. Bayesian ridge regression showed good cross-validated performance, and high reliability (R2 = 35%) and low bias (mean absolute prediction error, MAPE = 0.4%) during the validation step. Hand-held optical meter-derived CI and Sentinel-2-derived fAPAR showed comparable effects on machine learning performance, but CI outperformed NDVI and was therefore considered as a good alternative to Sentinel-2's fAPAR. The best times to sample the vegetation indices were the months of June (second half) and July. The results obtained in this work will serve several purposes including improvements in plant breeding, farming management and sorghum biomass yield forecasting at extension services and policy making levels.
format	article
author	Ephrem Habyarimana Faheem S Baloch
author_facet	Ephrem Habyarimana Faheem S Baloch
author_sort	Ephrem Habyarimana
title	Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
title_short	Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
title_full	Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
title_fullStr	Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
title_full_unstemmed	Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
title_sort	machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/cd3633b59c6e43f18b8c3b1fe1adf9f5
work_keys_str_mv	AT ephremhabyarimana machinelearningmodelsbasedonremoteandproximalsensingaspotentialmethodsforinseasonbiomassyieldspredictionincommercialsorghumfields AT faheemsbaloch machinelearningmodelsbasedonremoteandproximalsensingaspotentialmethodsforinseasonbiomassyieldspredictionincommercialsorghumfields
_version_	1718413892907958272

Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.

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