Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks

The article shows the possibility of using modern methods of artificial intelligence to calculate the yield of biomass of crops according to the given set input data (fertilizer doses, agrochemical parameters of the soil, productivity). The study reflects the results of testing a model of a compute...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Vasyl Ivanovych Lopushniak, Halyna Myhaylovna Hrytsuliak, Anatoliy Viktorovych Bykin, Nadia Petryvna Bordyuzha, Larysa Oleksandryvna Semenko, Myroslava Stepanivna Polutrenko, Yulia Zinoviyivna Kotsyubynska
Formato:	article
Lenguaje:	EN
Publicado:	Polish Society of Ecological Engineering (PTIE) 2021
Materias:	biomass artificial neural networks productivity precipitate artificial intelligence switchgrass Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/09a53f710fc643749422b8b4b8edcebb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:09a53f710fc643749422b8b4b8edcebb
record_format	dspace
spelling	oai:doaj.org-article:09a53f710fc643749422b8b4b8edcebb2021-11-04T08:03:23ZPrognostic Models of Panicum virgatum L. Using Artificial Neural Networks2299-899310.12911/22998993/142958https://doaj.org/article/09a53f710fc643749422b8b4b8edcebb2021-12-01T00:00:00Zhttp://www.jeeng.net/Prognostic-Models-of-Panicum-virgatum-L-Using-Artificial-Neural-Networks,142958,0,2.htmlhttps://doaj.org/toc/2299-8993The article shows the possibility of using modern methods of artificial intelligence to calculate the yield of biomass of crops according to the given set input data (fertilizer doses, agrochemical parameters of the soil, productivity). The study reflects the results of testing a model of a computer program of an artificial neural network, which allowed forecasting the yield of Panicum virgatum L (Switchgrass) depending on the joint application of fertilizers mineral and precipitate. On the basis of the calculations, the obtained model of productivity of vegetative mass of switchgrass shows a high level of forecasting efficiency (up to 97%). According to the results of experimental studies, the use of sewage sludge at a doses of 20 – 40 t/ha provides a dry biomass yield of Panicum virgatum L (Switchgrass) in the range of 13.1 - 20.3 t/ha, which is 3.4 – 7.2 t/ha more than in the option without fertilizer.Vasyl Ivanovych LopushniakHalyna Myhaylovna HrytsuliakAnatoliy Viktorovych BykinNadia Petryvna BordyuzhaLarysa Oleksandryvna SemenkoMyroslava Stepanivna PolutrenkoYulia Zinoviyivna KotsyubynskaPolish Society of Ecological Engineering (PTIE)articlebiomassartificial neural networksproductivityprecipitateartificial intelligenceswitchgrassEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Ecological Engineering, Vol 22, Iss 11, Pp 62-71 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	biomass artificial neural networks productivity precipitate artificial intelligence switchgrass Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	biomass artificial neural networks productivity precipitate artificial intelligence switchgrass Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Vasyl Ivanovych Lopushniak Halyna Myhaylovna Hrytsuliak Anatoliy Viktorovych Bykin Nadia Petryvna Bordyuzha Larysa Oleksandryvna Semenko Myroslava Stepanivna Polutrenko Yulia Zinoviyivna Kotsyubynska Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
description	The article shows the possibility of using modern methods of artificial intelligence to calculate the yield of biomass of crops according to the given set input data (fertilizer doses, agrochemical parameters of the soil, productivity). The study reflects the results of testing a model of a computer program of an artificial neural network, which allowed forecasting the yield of Panicum virgatum L (Switchgrass) depending on the joint application of fertilizers mineral and precipitate. On the basis of the calculations, the obtained model of productivity of vegetative mass of switchgrass shows a high level of forecasting efficiency (up to 97%). According to the results of experimental studies, the use of sewage sludge at a doses of 20 – 40 t/ha provides a dry biomass yield of Panicum virgatum L (Switchgrass) in the range of 13.1 - 20.3 t/ha, which is 3.4 – 7.2 t/ha more than in the option without fertilizer.
format	article
author	Vasyl Ivanovych Lopushniak Halyna Myhaylovna Hrytsuliak Anatoliy Viktorovych Bykin Nadia Petryvna Bordyuzha Larysa Oleksandryvna Semenko Myroslava Stepanivna Polutrenko Yulia Zinoviyivna Kotsyubynska
author_facet	Vasyl Ivanovych Lopushniak Halyna Myhaylovna Hrytsuliak Anatoliy Viktorovych Bykin Nadia Petryvna Bordyuzha Larysa Oleksandryvna Semenko Myroslava Stepanivna Polutrenko Yulia Zinoviyivna Kotsyubynska
author_sort	Vasyl Ivanovych Lopushniak
title	Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
title_short	Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
title_full	Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
title_fullStr	Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
title_full_unstemmed	Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks
title_sort	prognostic models of panicum virgatum l. using artificial neural networks
publisher	Polish Society of Ecological Engineering (PTIE)
publishDate	2021
url	https://doaj.org/article/09a53f710fc643749422b8b4b8edcebb
work_keys_str_mv	AT vasylivanovychlopushniak prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks AT halynamyhaylovnahrytsuliak prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks AT anatoliyviktorovychbykin prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks AT nadiapetryvnabordyuzha prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks AT larysaoleksandryvnasemenko prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks AT myroslavastepanivnapolutrenko prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks AT yuliazinoviyivnakotsyubynska prognosticmodelsofpanicumvirgatumlusingartificialneuralnetworks
_version_	1718445002467573760

Prognostic Models of Panicum virgatum L. Using Artificial Neural Networks

Ejemplares similares