Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset

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cg.contributor.affiliationInternational Center for Agricultural Research in the Dry Areasen_US
cg.contributor.affiliationUniversity of Kasselen_US
cg.contributor.affiliationNational Water Research Centeren_US
cg.contributor.affiliationCairo Universityen_US
cg.contributor.affiliationAgricultural Research Center, Soil, Water and Environment Research Instituteen_US
cg.contributor.affiliationJulius Kühn-Instituten_US
cg.contributor.donorCGIAR Trust Funden_US
cg.contributor.initiativeExcellence in Agronomyen_US
cg.coverage.countryEgypten_US
cg.coverage.iso3166-alpha2EGen_US
cg.coverage.regionNorthern Africaen_US
cg.creator.identifierGovind, Ajit: 0000-0002-0656-0004en_US
cg.creator.identifierNangia, Vinay: 0000-0001-5148-8614en_US
cg.creator.identifierDevkota Wasti, Mina: 0000-0002-2348-4816en_US
cg.creator.identifierOmar, Mohie: 0000-0003-0525-5398en_US
cg.identifier.doihttps://doi.org/10.1088/2515-7620/ad2d02en_US
cg.isijournalISI Journalen_US
cg.issue4en_US
cg.journalEnvironmental Research Communicationsen_US
cg.reviewStatusPeer Reviewen_US
cg.subject.actionAreaResilient Agrifood Systemsen_US
cg.subject.impactAreaClimate adaptation and mitigationen_US
cg.subject.sdgSDG 13 - Climate actionen_US
cg.volume6en_US
dc.contributor.authorKheir, Ahmed M.S.en_US
dc.contributor.authorGovind, Ajiten_US
dc.contributor.authorNangia, Vinayen_US
dc.contributor.authorDevkota Wasti, Minaen_US
dc.contributor.authorElnashar, Abdelrazeken_US
dc.contributor.authorOmar, Mohieen_US
dc.contributor.authorFeike, Tilen_US
dc.date.accessioned2025-01-29T15:14:43Zen_US
dc.date.available2025-01-29T15:14:43Zen_US
dc.identifier.urihttps://hdl.handle.net/10568/172410en_US
dc.titleDeveloping automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataseten_US
dcterms.abstractEstimating smallholder crop yields robustly and timely is crucial for improving agronomic practices, determining yield gaps, guiding investment, and policymaking to ensure food security. However, there is poor estimation of yield for most smallholders due to lack of technology, and field scale data, particularly in Egypt. Automated machine learning (AutoML) can be used to automate the machine learning workflow, including automatic training and optimization of multiple models within a userspecified time frame, but it has less attention so far. Here, we combined extensive field survey yield across wheat cultivated area in Egypt with diverse dataset of remote sensing, soil, and weather to predict field-level wheat yield using 22 Ml models in AutoML. The models showed robust accuracies for yield predictions, recording Willmott degree of agreement, (d>0.80) with higher accuracy when super learner (stacked ensemble) was used (R2=0.51, d=0.82). The trained AutoML was deployed to predict yield using remote sensing (RS) vegetative indices (VIs), demonstrating a good correlation with actual yield (R2=0.7). This is very important since it is considered a low-cost tool and could be used to explore early yield predictions. Since climate change has negative impacts on agricultural production and food security with some uncertainties, AutoML was deployed to predict wheat yield under recent climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6). These scenarios included single downscaled General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP2-4.5and SSP5-8.5during the mid-term period (2050). The stacked ensemble model displayed declines in yield of 21% and5%under SSP5-8.5 and SSP2-4.5 respectively during mid-century, with higher uncertainty under the highest emission scenario (SSP5- 8.5). The developed approach could be used as a rapid, accurate and low-cost method to predict yield for stakeholder farms all over the world where ground data is scarce.en_US
dcterms.accessRightsOpen Accessen_US
dcterms.available2024-04-25en_US
dcterms.bibliographicCitationAhmed M. S. Kheir, Ajit Govind, Vinay Nangia, Mina Devkota Wasti, Abdelrazek Elnashar, Mohie Omar, Til Feike. (25/4/2024). Developing automated machine learning approach for fast and robust crop yield prediction using a fusion of remote sensing, soil, and weather dataset. Environmental Research Communications, 6 (4).en_US
dcterms.formatPDFen_US
dcterms.issued2024-04-25en_US
dcterms.languageenen_US
dcterms.licenseCC-BY-4.0en_US
dcterms.publisherIOP Publishingen_US
dcterms.subjectclimate changeen_US
dcterms.subjectremote sensingen_US
dcterms.subjectmachine learningen_US
dcterms.subjectyield predictionen_US
dcterms.subjectwheaten_US
dcterms.typeJournal Articleen_US

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