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
Front-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 informa...
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2020
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/110670 |
| _version_ | 1855537195531632640 |
|---|---|
| author | Gómez Selvaraj, Michael Vergara, Alejandro Montenegro, Frank Alonso Ruiz, Henry Safari, Nancy Raymaekers, Dries Ocimati, Walter Ntamwira, Jules Bagula Tits, Laurent Omondi, Bonaventure Aman Oduor Blomme, Guy |
| author_browse | Alonso Ruiz, Henry Blomme, Guy Gómez Selvaraj, Michael Montenegro, Frank Ntamwira, Jules Bagula Ocimati, Walter Omondi, Bonaventure Aman Oduor Raymaekers, Dries Safari, Nancy Tits, Laurent Vergara, Alejandro |
| author_facet | Gómez Selvaraj, Michael Vergara, Alejandro Montenegro, Frank Alonso Ruiz, Henry Safari, Nancy Raymaekers, Dries Ocimati, Walter Ntamwira, Jules Bagula Tits, Laurent Omondi, Bonaventure Aman Oduor Blomme, Guy |
| author_sort | Gómez Selvaraj, Michael |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Front-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 |
| format | Journal Article |
| id | CGSpace110670 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1106702025-11-11T19:03:59Z 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 Gómez Selvaraj, Michael Vergara, Alejandro Montenegro, Frank Alonso Ruiz, Henry Safari, Nancy Raymaekers, Dries Ocimati, Walter Ntamwira, Jules Bagula Tits, Laurent Omondi, Bonaventure Aman Oduor Blomme, Guy artificial intelligence machine learning remote sensing disease recognition satellite imagery disease surveillance classification bananas inteligencia artificial aprendizaje electrónico vigilancia de enfermedades imágenes por satélites Front-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 2020-11 2020-12-30T17:58:09Z 2020-12-30T17:58:09Z Journal Article https://hdl.handle.net/10568/110670 en Open Access application/pdf Elsevier Gomez 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. |
| spellingShingle | artificial intelligence machine learning remote sensing disease recognition satellite imagery disease surveillance classification bananas inteligencia artificial aprendizaje electrónico vigilancia de enfermedades imágenes por satélites Gómez Selvaraj, Michael Vergara, Alejandro Montenegro, Frank Alonso Ruiz, Henry Safari, Nancy Raymaekers, Dries Ocimati, Walter Ntamwira, Jules Bagula Tits, Laurent Omondi, Bonaventure Aman Oduor Blomme, Guy 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 |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | artificial intelligence machine learning remote sensing disease recognition satellite imagery disease surveillance classification bananas inteligencia artificial aprendizaje electrónico vigilancia de enfermedades imágenes por satélites |
| url | https://hdl.handle.net/10568/110670 |
| work_keys_str_mv | AT gomezselvarajmichael detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT vergaraalejandro detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT montenegrofrank detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT alonsoruizhenry detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT safarinancy detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT raymaekersdries detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT ocimatiwalter detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT ntamwirajulesbagula detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT titslaurent detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT omondibonaventureamanoduor detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin AT blommeguy detectionofbananaplantsandtheirmajordiseasesthroughaerialimagesandmachinelearningmethodsacasestudyindrcongoandrepublicofbenin |