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Crop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Scheduling

2021Journal article Open access
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dc.contributor.authorAlibabaei, Khadijeh
dc.contributor.authorGaspar, Pedro Dinis
dc.contributor.authorLima, Tânia M.
dc.date.accessioned2022-01-07T13:08:03Z
dc.date.available2022-01-07T13:08:03Z
dc.date.issued2021
dc.description.abstractDeep learning has already been successfully used in the development of decision support systems in various domains. Therefore, there is an incentive to apply it in other important domains such as agriculture. Fertilizers, electricity, chemicals, human labor, and water are the components of total energy consumption in agriculture. Yield estimates are critical for food security, crop management, irrigation scheduling, and estimating labor requirements for harvesting and storage. Therefore, estimating product yield can reduce energy consumption. Two deep learning models, Long Short-Term Memory and Gated Recurrent Units, have been developed for the analysis of time-series data such as agricultural datasets. In this paper, the capabilities of these models and their extensions, called Bidirectional Long Short-Term Memory and Bidirectional Gated Recurrent Units, to predict end-of-season yields are investigated. The models use historical data, including climate data, irrigation scheduling, and soil water content, to estimate end-of-season yield. The application of this technique was tested for tomato and potato yields at a site in Portugal. The Bidirectional Long Short-Term memory outperformed the Gated Recurrent Units network, the Long Short-Term Memory, and the Bidirectional Gated Recurrent Units network on the validation dataset. The model was able to capture the nonlinear relationship between irrigation amount, climate data, and soil water content and predict yield with an MSE of 0.017 to 0.039. The performance of the Bidirectional Long Short-Term Memory in the test was compared with the most commonly used deep learning method, the Convolutional Neural Network, and machine learning methods including a Multi-Layer Perceptrons model and Random Forest Regression. The Bidirectional Long Short-Term Memory outperformed the other models with an R2 score between 0.97 and 0.99. The results show that analyzing agricultural data with the Long Short-Term Memory model improves the performance of the model in terms of accuracy. The Convolutional Neural Network model achieved the second-best performance. Therefore, the deep learning model has a remarkable ability to predict the yield at the end of the season.pt_PT
dc.description.sponsorshipProject Centro-01-0145-FEDER000017-EMaDeS-Energy, Materials, and Sustainable Development, co-funded by the Portugal 2020 Program (PT 2020), within the Regional Operational Program of the Center (CENTRO 2020) and the EU through the European Regional Development Fund (ERDF). Fundação para a Ciência e a Tecnologia (FCT—MCTES) also provided financial support via project UIDB/00151/2020 (C-MAST).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/en14113004pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/11578
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCentre for Mechanical and Aerospace Science and Technologies
dc.subjectAgriculturept_PT
dc.subjectDeep Learningpt_PT
dc.subjectSupport decision-making algorithmspt_PT
dc.subjectYield estimationpt_PT
dc.titleCrop Yield Estimation Using Deep Learning Based on Climate Big Data and Irrigation Schedulingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleCentre for Mechanical and Aerospace Science and Technologies
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00151%2F2020/PT
oaire.citation.issue11pt_PT
oaire.citation.startPage3004pt_PT
oaire.citation.titleEnergiespt_PT
oaire.citation.volume14pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameAlibabaei
person.familyNameGaspar
person.familyNameLima
person.givenNameKhadijeh
person.givenNamePedro Dinis
person.givenNameTânia
person.identifier1710267
person.identifier.ciencia-id6111-9F05-2916
person.identifier.ciencia-id771E-3B60-A936
person.identifier.orcid0000-0002-2319-8211
person.identifier.orcid0000-0003-1691-1709
person.identifier.orcid0000-0002-7540-3854
person.identifier.ridN-3016-2013
person.identifier.ridV-5052-2017
person.identifier.scopus-author-id57419570900
person.identifier.scopus-author-id48661120000
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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