Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

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dc.creatorGómez Selvaraj, Michael
dc.creatorVergara, Alejandro
dc.creatorMontenegro, Frank
dc.creatorAlonso Ruiz, Henry
dc.creatorSafari, Nancy
dc.creatorRaymaekers, Dries
dc.creatorOcimati, Walter
dc.creatorNtamwira, Jules Bagula
dc.creatorTits, Laurent
dc.creatorOmondi, Bonaventure Aman Oduor
dc.creatorBlomme, Guy
dc.date2020-11
dc.date2020-12-30T17:58:09Z
dc.date2020-12-30T17:58:09Z
dc.date.accessioned2026-06-27T13:34:41Z
dc.descriptionFront-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banana (BXW), healthy banana cluster and individual banana plants, respectively. These approaches of aerial image-based ML models have high potential to provide a decision support system for major banana diseases in Africa
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/110670
dc.identifier.urihttp://hdl.handle.net/123456789/63778
dc.languageen
dc.publisherElsevier
dc.rightsOpen Access
dc.sourceGomez Selvaraj, M.; Vergara, A.; Montenegro, F.; Alonso Ruiz, H.; Safari, N.; Raymaekers, D.; Ocimati, W.; Ntamwira, J.; Tits, L.; Omondi, A.B.; Blomme, G. (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS Journal of Photogrammetry and Remote Sensing 169 p. 110-124. ISSN: 1872-8235.
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectremote sensing
dc.subjectdisease recognition
dc.subjectsatellite imagery
dc.subjectdisease surveillance
dc.subjectclassification
dc.subjectbananas
dc.subjectinteligencia artificial
dc.subjectaprendizaje electrónico
dc.subjectvigilancia de enfermedades
dc.subjectimágenes por satélites
dc.titleDetection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin
dc.typeJournal Article

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