Predictive Analytics of Plots For Soybean Production

Autores
Mavolo, Luca; Xodo, Daniel; Mavolo, Pablo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This study is to determine the feasible productivity of a plot of land in large fields where the quality of the soil and the weather conditions fluctuate every year, hindering optimum soybean production practices. The aim is to predict 8 sceneries through the artificial neural network model and study its reliability. Then predict 7 feasible sceneries to achieve a good sowing strategy on certain plots of land and with certain types of seeds. Finally, to make a prediction using the average historical rainfall data collected during the studied months and to observe the fluctuations on the yield in accordance with previous predictions. The artificial neural network is the method used and it was provided by soft RISK Industrial 7.6 (Neural Tools). The result is going to be compared with the data collected from the company “Nueva Castilla” of Trenque Lauquen (Buenos Aires province, Argentina) to determine the practical and technical feasibility of the model. These data correspond to more than 17 years of climate and weather analysis, soil and soybean yield with different types of seeds.
Fil: Mavolo Luca. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; Argentina
Fil: Xodo Daniel. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; Argentina
Fil: Mavolo Pablo. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; Argentina
Peer Reviewed
Materia
Artificial neural network
soybean (glycine max)
rainfall, yield prediction
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2023-12-04T18:22:24Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/9022
Acceder
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spelling Predictive Analytics of Plots For Soybean ProductionMavolo, LucaXodo, DanielMavolo, PabloArtificial neural networksoybean (glycine max)rainfall, yield predictionThis study is to determine the feasible productivity of a plot of land in large fields where the quality of the soil and the weather conditions fluctuate every year, hindering optimum soybean production practices. The aim is to predict 8 sceneries through the artificial neural network model and study its reliability. Then predict 7 feasible sceneries to achieve a good sowing strategy on certain plots of land and with certain types of seeds. Finally, to make a prediction using the average historical rainfall data collected during the studied months and to observe the fluctuations on the yield in accordance with previous predictions. The artificial neural network is the method used and it was provided by soft RISK Industrial 7.6 (Neural Tools). The result is going to be compared with the data collected from the company “Nueva Castilla” of Trenque Lauquen (Buenos Aires province, Argentina) to determine the practical and technical feasibility of the model. These data correspond to more than 17 years of climate and weather analysis, soil and soybean yield with different types of seeds.Fil: Mavolo Luca. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; ArgentinaFil: Xodo Daniel. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; ArgentinaFil: Mavolo Pablo. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; ArgentinaPeer Reviewed2023-12-04T18:22:24Z2023-12-04T18:22:24Z2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdf2315-7739http://hdl.handle.net/20.500.12272/90221015413/ajar.2020.0130engenginfo:eu-repo/semantics/openAccess2023-12-04T18:22:24ZNo permitir el uso comercial de la obra No permitir modificaciones de la obrareponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:46:28Zoai:ria.utn.edu.ar:20.500.12272/9022instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:46:29.767Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Predictive Analytics of Plots For Soybean Production
title Predictive Analytics of Plots For Soybean Production
spellingShingle Predictive Analytics of Plots For Soybean Production
Mavolo, Luca
Artificial neural network
soybean (glycine max)
rainfall, yield prediction
title_short Predictive Analytics of Plots For Soybean Production
title_full Predictive Analytics of Plots For Soybean Production
title_fullStr Predictive Analytics of Plots For Soybean Production
title_full_unstemmed Predictive Analytics of Plots For Soybean Production
title_sort Predictive Analytics of Plots For Soybean Production
dc.creator.none.fl_str_mv Mavolo, Luca
Xodo, Daniel
Mavolo, Pablo
author Mavolo, Luca
author_facet Mavolo, Luca
Xodo, Daniel
Mavolo, Pablo
author_role author
author2 Xodo, Daniel
Mavolo, Pablo
author2_role author
author
dc.subject.none.fl_str_mv Artificial neural network
soybean (glycine max)
rainfall, yield prediction
topic Artificial neural network
soybean (glycine max)
rainfall, yield prediction
dc.description.none.fl_txt_mv This study is to determine the feasible productivity of a plot of land in large fields where the quality of the soil and the weather conditions fluctuate every year, hindering optimum soybean production practices. The aim is to predict 8 sceneries through the artificial neural network model and study its reliability. Then predict 7 feasible sceneries to achieve a good sowing strategy on certain plots of land and with certain types of seeds. Finally, to make a prediction using the average historical rainfall data collected during the studied months and to observe the fluctuations on the yield in accordance with previous predictions. The artificial neural network is the method used and it was provided by soft RISK Industrial 7.6 (Neural Tools). The result is going to be compared with the data collected from the company “Nueva Castilla” of Trenque Lauquen (Buenos Aires province, Argentina) to determine the practical and technical feasibility of the model. These data correspond to more than 17 years of climate and weather analysis, soil and soybean yield with different types of seeds.
Fil: Mavolo Luca. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; Argentina
Fil: Xodo Daniel. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; Argentina
Fil: Mavolo Pablo. Universidad Tecnológica Nacional. Facultad Regional Trenque Lauquen; Argentina
Peer Reviewed
description This study is to determine the feasible productivity of a plot of land in large fields where the quality of the soil and the weather conditions fluctuate every year, hindering optimum soybean production practices. The aim is to predict 8 sceneries through the artificial neural network model and study its reliability. Then predict 7 feasible sceneries to achieve a good sowing strategy on certain plots of land and with certain types of seeds. Finally, to make a prediction using the average historical rainfall data collected during the studied months and to observe the fluctuations on the yield in accordance with previous predictions. The artificial neural network is the method used and it was provided by soft RISK Industrial 7.6 (Neural Tools). The result is going to be compared with the data collected from the company “Nueva Castilla” of Trenque Lauquen (Buenos Aires province, Argentina) to determine the practical and technical feasibility of the model. These data correspond to more than 17 years of climate and weather analysis, soil and soybean yield with different types of seeds.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023-12-04T18:22:24Z
2023-12-04T18:22:24Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv 2315-7739
http://hdl.handle.net/20.500.12272/9022
1015413/ajar.2020.0130
identifier_str_mv 2315-7739
1015413/ajar.2020.0130
url http://hdl.handle.net/20.500.12272/9022
dc.language.none.fl_str_mv eng
eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2023-12-04T18:22:24Z
No permitir el uso comercial de la obra No permitir modificaciones de la obra
eu_rights_str_mv openAccess
rights_invalid_str_mv 2023-12-04T18:22:24Z
No permitir el uso comercial de la obra No permitir modificaciones de la obra
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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