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dc.contributor.author
Jimenez, Andres F.
dc.contributor.author
Ortiz, Brenda V.
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Bondesan, Luca
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Morata, Guilherme
dc.contributor.author
Damianidis, Damianos
dc.date.available
2022-10-24T15:49:53Z
dc.date.issued
2020-09
dc.identifier.citation
Jimenez, Andres F.; Ortiz, Brenda V.; Bondesan, Luca; Morata, Guilherme; Damianidis, Damianos; Long short-term memory neural network for irrigation management: a case study from Southern Alabama, USA; Springer; Precision Agriculture; 22; 2; 9-2020; 475-492
dc.identifier.issn
1385-2256
dc.identifier.uri
http://hdl.handle.net/11336/174589
dc.description.abstract
The metabolism and growth of vegetation are highly dependent on the changes in soil water content. Irrigation scheduling and application of water at the right time and rate are a key aspect for precision irrigation. In this study, the Long Short-Term Memory (LSTM) Neural Network model was studied to predict irrigation prescriptions for 1, 3, 6, 12 and 24 h in advance. Training data for LSTM were collected from a precision irrigation study conducted in Alabama, USA. The prediction estimation of irrigation prescription used soil matric potential data measured within two contrasting soil types. Performance of the LSTM models were evaluated by comparing neural network parameters and prediction capability by soil type. The optimal learning algorithm for each case was also determined. The LSTM Neural Network showed good prediction capabilities for both soil types, with R2 ranging between 0.82 and 0.98 for one hour ahead prescription and getting smaller as prediction time increases. The irrigation rate prediction was verified by actual observations that demonstrate the suitability of the machine learning technique as a decision-support tool for irrigation scheduling.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
CORN
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IRRIGATION PRESCRIPTION
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LONG-SHORT TERM MEMORY NEURAL NETWORK
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PRECISION IRRIGATION
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SOIL MATRIC POTENTIAL
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Agricultura
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Agricultura, Silvicultura y Pesca
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CIENCIAS AGRÍCOLAS
dc.title
Long short-term memory neural network for irrigation management: a case study from Southern Alabama, USA
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-22T15:07:42Z
dc.journal.volume
22
dc.journal.number
2
dc.journal.pagination
475-492
dc.journal.pais
Alemania
dc.journal.ciudad
Berlín
dc.description.fil
Fil: Jimenez, Andres F.. Auburn University.; Estados Unidos. Universidad Nacional de Colombia; Colombia. Universidad de Los Llanos; Colombia
dc.description.fil
Fil: Ortiz, Brenda V.. Auburn University.; Estados Unidos
dc.description.fil
Fil: Bondesan, Luca. Auburn University.; Estados Unidos. Università di Padova; Italia
dc.description.fil
Fil: Morata, Guilherme. Auburn University.; Estados Unidos
dc.description.fil
Fil: Damianidis, Damianos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina. Auburn University.; Estados Unidos
dc.journal.title
Precision Agriculture
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s11119-020-09753-z
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s11119-020-09753-z
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