Crop yield prediction integrating genotype and weather variables using deep learning.

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybe...

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Autores principales:	Johnathon Shook, Tryambak Gangopadhyay, Linjiang Wu, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh K Singh
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2725d1c2b5ff4022aef0b56c9f7c68bc
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spelling	oai:doaj.org-article:2725d1c2b5ff4022aef0b56c9f7c68bc2021-11-25T06:23:31ZCrop yield prediction integrating genotype and weather variables using deep learning.1932-620310.1371/journal.pone.0252402https://doaj.org/article/2725d1c2b5ff4022aef0b56c9f7c68bc2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252402https://doaj.org/toc/1932-6203Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)-Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.Johnathon ShookTryambak GangopadhyayLinjiang WuBaskar GanapathysubramanianSoumik SarkarAsheesh K SinghPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252402 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Johnathon Shook Tryambak Gangopadhyay Linjiang Wu Baskar Ganapathysubramanian Soumik Sarkar Asheesh K Singh Crop yield prediction integrating genotype and weather variables using deep learning.
description	Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)-Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.
format	article
author	Johnathon Shook Tryambak Gangopadhyay Linjiang Wu Baskar Ganapathysubramanian Soumik Sarkar Asheesh K Singh
author_facet	Johnathon Shook Tryambak Gangopadhyay Linjiang Wu Baskar Ganapathysubramanian Soumik Sarkar Asheesh K Singh
author_sort	Johnathon Shook
title	Crop yield prediction integrating genotype and weather variables using deep learning.
title_short	Crop yield prediction integrating genotype and weather variables using deep learning.
title_full	Crop yield prediction integrating genotype and weather variables using deep learning.
title_fullStr	Crop yield prediction integrating genotype and weather variables using deep learning.
title_full_unstemmed	Crop yield prediction integrating genotype and weather variables using deep learning.
title_sort	crop yield prediction integrating genotype and weather variables using deep learning.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/2725d1c2b5ff4022aef0b56c9f7c68bc
work_keys_str_mv	AT johnathonshook cropyieldpredictionintegratinggenotypeandweathervariablesusingdeeplearning AT tryambakgangopadhyay cropyieldpredictionintegratinggenotypeandweathervariablesusingdeeplearning AT linjiangwu cropyieldpredictionintegratinggenotypeandweathervariablesusingdeeplearning AT baskarganapathysubramanian cropyieldpredictionintegratinggenotypeandweathervariablesusingdeeplearning AT soumiksarkar cropyieldpredictionintegratinggenotypeandweathervariablesusingdeeplearning AT asheeshksingh cropyieldpredictionintegratinggenotypeandweathervariablesusingdeeplearning
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Crop yield prediction integrating genotype and weather variables using deep learning.

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