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dc.contributor.authorAlabi, T.R.
dc.contributor.authorAdewopo, J.
dc.contributor.authorDuke, O.P.
dc.contributor.authorKumar, P.L.
dc.date.accessioned2022-12-02T09:46:11Z
dc.date.available2022-12-02T09:46:11Z
dc.date.issued2022
dc.identifier.citationAlabi, T.R., Adewopo, J., Duke, O.P. & Kumar, P.L. (2022). Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease surveillance. Remote Sensing, 14(20):5206, 1-22.
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7952
dc.description.abstractBanana (and plantain, Musa spp.), in sub-Saharan Africa (SSA), is predominantly grown as a mixed crop by smallholder farmers in backyards and small farmlands, typically ranging from 0.2 ha to 3 ha. The crop is affected by several pests and diseases, including the invasive banana bunchy top virus (BBTV, genus Babuvirus), which is emerging as a major threat to banana production in SSA. The BBTV outbreak in West Africa was first recorded in the Benin Republic in 2010 and has spread to the adjoining territories of Nigeria and Togo. Regular surveillance, conducted as part of the containment efforts, requires the identification of banana fields for disease assessment. However, small and fragmented production spread across large areas poses complications for identifying all banana farms using conventional field survey methods, which is also time-consuming and expensive. In this study, we developed a remote sensing approach and machine learning (ML) models that can be used to identify banana fields for targeted BBTV surveillance. We used medium-resolution synthetic aperture radar (SAR), Sentinel 2A satellite imagery, and high-resolution RGB and multispectral aerial imagery from an unmanned aerial vehicle (UAV) to develop an operational banana mapping framework by combining the UAV, SAR, and Sentinel 2A data with the Support Vector Machine (SVM) and Random Forest (RF) machine learning algorithms. The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. The user accuracy (UA) and producer accuracy (PA) for the banana class were 83% and 78%, respectively. The BBTV surveillance teams used the banana mapping framework to identify banana fields in the BBTV-affected southwest Ogun state of Nigeria, which helped in detecting 17 sites with BBTV infection. These findings suggest that the prediction of banana and other crops in the heterogeneous smallholder farming systems is feasible, with the precision necessary to guide BBTV surveillance in large areas in SSA.
dc.description.sponsorshipCGIAR Research Program on Roots, Tubers, and Banana
dc.description.sponsorshipCGIAR Plant Health Initiative
dc.description.sponsorshipCGIAR Trust Fund Donors
dc.description.sponsorshipUniversity of Queensland Project
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-22
dc.language.isoen
dc.subjectMusa
dc.subjectBananas
dc.subjectPlantains
dc.subjectSmallholders
dc.subjectFarmers
dc.subjectRemote Sensing
dc.subjectDrones
dc.subjectMachine Learning
dc.subjectDisease Surveillance
dc.subjectBanana Bunchy Top Virus
dc.subjectAfrica
dc.titleBanana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease surveillance
dc.typeJournal Article
cg.contributor.crpMaize
cg.contributor.crpRoots, Tubers and Bananas
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationTERI School of Advanced Studies, India
cg.coverage.regionAfrica
cg.coverage.regionWest Africa
cg.coverage.countryNigeria
cg.coverage.countryTogo
cg.coverage.hubHeadquarters and Western Africa Hub
cg.researchthemeNatural Resource Management
cg.researchthemePlant Production and Health
cg.identifier.bibtexciteidALABI:2022a
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectBanana
cg.iitasubjectDisease Control
cg.iitasubjectFarming Systems
cg.iitasubjectFood Security
cg.iitasubjectPlant Breeding
cg.iitasubjectPlant Health
cg.iitasubjectPlant Production
cg.iitasubjectPlantain
cg.iitasubjectSmallholder Farmers
cg.journalRemote Sensing
cg.notesOpen Access Article; Published online: 18 Oct 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.3390/rs14205206
cg.iitaauthor.identifierJulius Adewopo: 0000-0003-4831-2823
cg.iitaauthor.identifierP. Lava Kumar: 0000-0003-4388-6510
cg.futureupdate.requiredNo
cg.identifier.issue20
cg.identifier.volume14


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