Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Mohsen Shahhosseini"'
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield predic
Externí odkaz:
https://doaj.org/article/d21839fd27114e5f93c9985a564de3d4
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better pr
Externí odkaz:
https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b
Publikováno v:
Machine Learning with Applications, Vol 7, Iss , Pp 100251- (2022)
Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been
Externí odkaz:
https://doaj.org/article/7944700205984ec187d0c72192837466
Publikováno v:
Frontiers in Plant Science, Vol 12 (2021)
We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and histor
Externí odkaz:
https://doaj.org/article/3fccf0d1568144478c709b03b4163d3b
Publikováno v:
Frontiers in Plant Science, Vol 11 (2020)
The emergence of new technologies to synthesize and analyze big data with high-performance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predic
Externí odkaz:
https://doaj.org/article/5e4ed90fef224b41b4b5b046553311a7
Publikováno v:
Environmental Research Letters, Vol 14, Iss 12, p 124026 (2019)
Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited
Externí odkaz:
https://doaj.org/article/a4ceb40c32754846b2aaf0d6ffe3d057
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Scientific Reports
Scientific Reports
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e17bf7a1c9f4bc9a194909fed7fa7e5b
https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b
https://doaj.org/article/33f06b500da54359bd032ea96d19cf9b
Autor:
Mohsen Shahhosseini, Guiping Hu
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030665005
Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models. The key in making well-performing ensemble model is in the diversity of the bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::982662cbb6da46ac00ec19a2f5a892f6
https://doi.org/10.1007/978-3-030-66501-2_4
https://doi.org/10.1007/978-3-030-66501-2_4
Publikováno v:
Frontiers in Plant Science, Vol 11 (2020)
Frontiers in Plant Science
Frontiers in Plant Science
The emergence of new technologies to synthesize and analyze big data with high-performance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0866a6728014cb2559f3165e5d4e794c
http://arxiv.org/abs/2001.09055
http://arxiv.org/abs/2001.09055
Publikováno v:
Smart Service Systems, Operations Management, and Analytics ISBN: 9783030309664
Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge, especially, in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f816c8e093ed291f5f65665906ee8dba
https://doi.org/10.1007/978-3-030-30967-1_9
https://doi.org/10.1007/978-3-030-30967-1_9