Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2
Fall 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 s...
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001633 |
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| author | Tatenda Dzurume Roshanak Darvishzadeh Timothy Dube T.S. Amjath Babu Mutasim Billah Syed Nurul Alam Mustafa Kamal Md. Harun-Or-Rashid Badal Chandra Biswas Md. Ashraf Uddin Md. Abdul Muyeed Md. Mostafizur Rahman Shah Timothy J. Krupnik Andrew Nelson |
| author_facet | Tatenda Dzurume Roshanak Darvishzadeh Timothy Dube T.S. Amjath Babu Mutasim Billah Syed Nurul Alam Mustafa Kamal Md. Harun-Or-Rashid Badal Chandra Biswas Md. Ashraf Uddin Md. Abdul Muyeed Md. Mostafizur Rahman Shah Timothy J. Krupnik Andrew Nelson |
| author_sort | Tatenda Dzurume |
| collection | DOAJ |
| description | Fall 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. |
| format | Article |
| id | doaj-art-04f3eafd7b8e44c6860247d78b0aa531 |
| institution | OA Journals |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-04f3eafd7b8e44c6860247d78b0aa5312025-08-20T01:49:12ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910451610.1016/j.jag.2025.104516Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2Tatenda Dzurume0Roshanak Darvishzadeh1Timothy Dube2T.S. Amjath Babu3Mutasim Billah4Syed Nurul Alam5Mustafa Kamal6Md. Harun-Or-Rashid7Badal Chandra Biswas8Md. Ashraf Uddin9Md. Abdul Muyeed10Md. Mostafizur Rahman Shah11Timothy J. Krupnik12Andrew Nelson13Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands; Institute for Water Studies, University of the Western Cape, Private Bag X17, Bellville 7535, South AfricaDepartment of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the NetherlandsInstitute for Water Studies, University of the Western Cape, Private Bag X17, Bellville 7535, South AfricaInternational Maize and Wheat Improvement Center (CIMMYT), Dhaka, BangladeshInternational Maize and Wheat Improvement Center (CIMMYT), Dhaka, BangladeshInternational Maize and Wheat Improvement Center (CIMMYT), Dhaka, BangladeshInternational Maize and Wheat Improvement Center (CIMMYT), Dhaka, BangladeshInternational Maize and Wheat Improvement Center (CIMMYT), Dhaka, BangladeshDepartment of Agricultural Extension, Dhaka, BangladeshDepartment of Agricultural Extension, Dhaka, BangladeshDepartment of Agricultural Extension, Dhaka, BangladeshBangladesh Wheat and Maize Research Institute, BangladeshInternational Maize and Wheat Improvement Center (CIMMYT), Dhaka, BangladeshDepartment of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the NetherlandsFall 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.http://www.sciencedirect.com/science/article/pii/S1569843225001633Invasive pestMaize (Zea mays)Remote SensingRandom Forest ClassifierPest Management |
| spellingShingle | Tatenda Dzurume Roshanak Darvishzadeh Timothy Dube T.S. Amjath Babu Mutasim Billah Syed Nurul Alam Mustafa Kamal Md. Harun-Or-Rashid Badal Chandra Biswas Md. Ashraf Uddin Md. Abdul Muyeed Md. Mostafizur Rahman Shah Timothy J. Krupnik Andrew Nelson Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 International Journal of Applied Earth Observations and Geoinformation Invasive pest Maize (Zea mays) Remote Sensing Random Forest Classifier Pest Management |
| title | Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 |
| title_full | Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 |
| title_fullStr | Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 |
| title_full_unstemmed | Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 |
| title_short | Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 |
| title_sort | detection of fall armyworm infestation in maize fields during vegetative growth stages using temporal sentinel 2 |
| topic | Invasive pest Maize (Zea mays) Remote Sensing Random Forest Classifier Pest Management |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001633 |
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