Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

Abstract Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield po...

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Autores principales:	Saeed Khaki, Hieu Pham, Lizhi Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bb39e63a6f074bcbb30adf1403b105e0
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spelling	oai:doaj.org-article:bb39e63a6f074bcbb30adf1403b105e02021-12-02T16:53:20ZSimultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning10.1038/s41598-021-89779-z2045-2322https://doaj.org/article/bb39e63a6f074bcbb30adf1403b105e02021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89779-zhttps://doaj.org/toc/2045-2322Abstract Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1132 counties for corn and 1076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with an MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.Saeed KhakiHieu PhamLizhi WangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Saeed Khaki Hieu Pham Lizhi Wang Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
description	Abstract Large-scale crop yield estimation is, in part, made possible due to the availability of remote sensing data allowing for the continuous monitoring of crops throughout their growth cycle. Having this information allows stakeholders the ability to make real-time decisions to maximize yield potential. Although various models exist that predict yield from remote sensing data, there currently does not exist an approach that can estimate yield for multiple crops simultaneously, and thus leads to more accurate predictions. A model that predicts the yield of multiple crops and concurrently considers the interaction between multiple crop yields. We propose a new convolutional neural network model called YieldNet which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Additionally, to consider the multi-target response variable, we propose a new loss function. We conduct our experiment using data from 1132 counties for corn and 1076 counties for soybean across the United States. Numerical results demonstrate that our proposed method accurately predicts corn and soybean yield from one to four months before the harvest with an MAE being 8.74% and 8.70% of the average yield, respectively, and is competitive to other state-of-the-art approaches.
format	article
author	Saeed Khaki Hieu Pham Lizhi Wang
author_facet	Saeed Khaki Hieu Pham Lizhi Wang
author_sort	Saeed Khaki
title	Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
title_short	Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
title_full	Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
title_fullStr	Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
title_full_unstemmed	Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
title_sort	simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bb39e63a6f074bcbb30adf1403b105e0
work_keys_str_mv	AT saeedkhaki simultaneouscornandsoybeanyieldpredictionfromremotesensingdatausingdeeptransferlearning AT hieupham simultaneouscornandsoybeanyieldpredictionfromremotesensingdatausingdeeptransferlearning AT lizhiwang simultaneouscornandsoybeanyieldpredictionfromremotesensingdatausingdeeptransferlearning
_version_	1718382883076308992

Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

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