Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2

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cg.contributor.affiliationUniversity of Twente
cg.contributor.affiliationUniversity of the Western Cape
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.contributor.affiliationDepartment of Agricultural Extension, Bangladesh
cg.contributor.affiliationBangladesh Wheat and Maize Research Institute
cg.contributor.donorCGIAR Trust Fund
cg.contributor.donorUnited States Agency for International Development
cg.contributor.initiativeTransforming Agrifood Systems in South Asia
cg.creator.identifierTimothy Dube: 0000-0003-3456-8991
cg.creator.identifierT.S Amjath-Babu: 0000-0001-9902-7104
cg.creator.identifierMustafa Kamal: 0000-0003-0473-5322
cg.creator.identifierMd. Harun-Or-Rashid: 0000-0001-8890-1396
cg.creator.identifierTimothy Joseph Krupnik: 0000-0001-6973-0106
cg.howPublishedFormally Published
cg.identifier.doihttps://doi.org/10.1016/j.jag.2025.104516
cg.identifier.urlhttps://hdl.handle.net/10883/35607
cg.isijournalISI Journal
cg.issn1569-8432||1872-826X
cg.journalInternational Journal of Applied Earth Observation and Geoinformation
cg.reviewStatusPeer Review
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.impactAreaNutrition, health and food security
dc.contributor.authorDzurume, Tatenda
dc.contributor.authorDarvishzadeh, Roshanak
dc.contributor.authorDube, Timothy
dc.contributor.authorAmjath Babu, T.S.
dc.contributor.authorBillah, Mutasim
dc.contributor.authorSyed Nurul Alam
dc.contributor.authorKamal, Mustafa
dc.contributor.authorMd. Harun-Or-Rashid
dc.contributor.authorBiswas, Badal Chandra
dc.contributor.authorMd. Ashraf Uddin
dc.contributor.authorMd. Abdul Muyeed
dc.contributor.authorMd Mostafizur Rahman Shah
dc.contributor.authorKrupnik, Timothy J.
dc.contributor.authorNelson, Andrew
dc.date.accessioned2025-04-29T15:14:56Z
dc.date.available2025-04-29T15:14:56Z
dc.identifier.urihttps://hdl.handle.net/10568/174380
dc.titleDetection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2
dcterms.abstractFall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified infestation severity in Bangladesh using Sentinel-2 satellite imagery and Random Forest (RF) classification. Field observations on FAW infestation severity (none, moderate, and severe), collected by the Bangladesh Department of Agricultural Extension during 2019 and 2020, were used to train the RF classifier. Six thousand nine hundred ninety-eight observations were collected from 579 maize fields through weekly scouting. The Kruskal-Wallis test and Dunn’s post-hoc test were applied to identify the most significant spectral bands (P < 0.05) for detecting FAW incidence and severity across different maize growth stages. The results demonstrated that the spectral reflectance from Sentinel-2 bands varied significantly among different classes of FAW infestation, with noticeable differences observed during the early developmental stages of maize (vegetative growth stages 3 to 8). RF identified nine spectral bands and two spectral vegetation indices as important for FAW infestation discrimination. The RF classifier was evaluated using five-fold cross-validation, achieving an overall accuracy between 74 % and 84 %. The independent test set’s accuracy ranged from 72 % to 82 %. The mean multiclass AUC ranged from 0.83 to 0.95. Moreover, the results demonstrated the feasibility of detecting the severity of FAW infestation using temporal Sentinel-2 data and machine learning techniques. These findings underscore the potential of remote sensing and machine learning techniques for effectively monitoring and managing crop pests. The study provides valuable insights for classifying FAW infestation using high-resolution multitemporal data.
dcterms.accessRightsOpen Access
dcterms.available2025-04-02
dcterms.bibliographicCitationDzurume, T., Darvishzadeh, R., Dube, T., Babu, T. S. A., Billah, M., Alam, S. N., Kamal, M., Harun-Or-Rashid, Md., Biswas, B. C., Uddin, Md. A., Muyeed, Md. A., Rahman Shah, Md. M., Krupnik, T. J., & Nelson, A. (2025). Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2. International Journal of Applied Earth Observation and Geoinformation, 139, 104516. https://doi.org/10.1016/j.jag.2025.104516
dcterms.issued2025-05
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.subjectpest insects
dcterms.subjectmaize
dcterms.subjectremote sensing
dcterms.subjectpest management
dcterms.subjectfall armyworms
dcterms.typeJournal Article

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