Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs

Bibliographic Details
Main Author: Dong, Ping
Publication Date: 2024
Other Authors: Wang, Ming, Li, Kuo, Qiao, Hongbo, Zhao, Yuyang, Bação, Fernando, Shi, Lei, Guo, Wei, Si, Haiping
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/171517
Summary: Dong, P., Wang, M., Li, K., Qiao, H., Zhao, Y., Bação, F., Shi, L., Guo, W., & Si, H. (2024). Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs. Drones, 8(9), 1-18. Article 445. https://doi.org/10.3390/drones8090445 --- This research was funded by the Key Research and Development Project of Henan Province, China (241111110800); Natural Science Foundation of Henan Province, China (232300420186); Key Scientific and Technological Project of Henan Province (242102111193); National Natural Science Foundation of China (32271993); Joint Fund of Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province, China (222301420113, 222301420114); Key Research and Development Project of Henan Province, China (231111110100); and Henan Center for Outstanding Overseas Scientists (Project No. GZS2024006). FCT (Fundação para a Ciência e a Tecnologia), under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
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spelling Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVsdisease identificationmultispectralfusarium head blightCA attention mechanismimage segmentationControl and Systems EngineeringInformation SystemsAerospace EngineeringComputer Science ApplicationsArtificial IntelligenceDong, P., Wang, M., Li, K., Qiao, H., Zhao, Y., Bação, F., Shi, L., Guo, W., & Si, H. (2024). Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs. Drones, 8(9), 1-18. Article 445. https://doi.org/10.3390/drones8090445 --- This research was funded by the Key Research and Development Project of Henan Province, China (241111110800); Natural Science Foundation of Henan Province, China (232300420186); Key Scientific and Technological Project of Henan Province (242102111193); National Natural Science Foundation of China (32271993); Joint Fund of Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province, China (222301420113, 222301420114); Key Research and Development Project of Henan Province, China (231111110100); and Henan Center for Outstanding Overseas Scientists (Project No. GZS2024006). FCT (Fundação para a Ciência e a Tecnologia), under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).Fusarium head blight (FHB), a severe ailment triggered by fungal pathogens, poses a considerable risk to both the yield and quality of winter wheat worldwide, underscoring the urgency for precise detection measures that can effectively mitigate and manage the spread of FHB. Addressing the limitations of current deep learning models in capturing detailed features from UAV imagery, this study proposes an advanced identification model for FHB in wheat based on multispectral imagery from UAVs. The model leverages the U2Net network as its baseline, incorporating the Coordinate Attention (CA) mechanism and the RFB-S (Receptive Field Block—Small) multi-scale feature extraction module. By integrating key spectral features from multispectral bands (SBs) and vegetation indices (VIs), the model enhances feature extraction capabilities and spatial information awareness. The CA mechanism is used to improve the model’s ability to express image features, while the RFB-S module increases the receptive field of convolutional layers, enhancing multi-scale spatial feature modeling. The results demonstrate that the improved U2Net model, termed U2Net-plus, achieves an identification accuracy of 91.73% for FHB in large-scale wheat fields, significantly outperforming the original model and other mainstream semantic segmentation models such as U-Net, SegNet, and DeepLabV3+. This method facilitates the rapid identification of large-scale FHB outbreaks in wheat, providing an effective approach for large-field wheat disease detection.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNDong, PingWang, MingLi, KuoQiao, HongboZhao, YuyangBação, FernandoShi, LeiGuo, WeiSi, Haiping2024-09-10T22:22:01Z2024-08-302024-08-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article18application/pdfhttp://hdl.handle.net/10362/171517eng2504-446XPURE: 99262757https://doi.org/10.3390/drones8090445info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-10-14T01:38:27Zoai:run.unl.pt:10362/171517Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:50:08.782180Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
title Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
spellingShingle Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
Dong, Ping
disease identification
multispectral
fusarium head blight
CA attention mechanism
image segmentation
Control and Systems Engineering
Information Systems
Aerospace Engineering
Computer Science Applications
Artificial Intelligence
title_short Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
title_full Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
title_fullStr Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
title_full_unstemmed Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
title_sort Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs
author Dong, Ping
author_facet Dong, Ping
Wang, Ming
Li, Kuo
Qiao, Hongbo
Zhao, Yuyang
Bação, Fernando
Shi, Lei
Guo, Wei
Si, Haiping
author_role author
author2 Wang, Ming
Li, Kuo
Qiao, Hongbo
Zhao, Yuyang
Bação, Fernando
Shi, Lei
Guo, Wei
Si, Haiping
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Dong, Ping
Wang, Ming
Li, Kuo
Qiao, Hongbo
Zhao, Yuyang
Bação, Fernando
Shi, Lei
Guo, Wei
Si, Haiping
dc.subject.por.fl_str_mv disease identification
multispectral
fusarium head blight
CA attention mechanism
image segmentation
Control and Systems Engineering
Information Systems
Aerospace Engineering
Computer Science Applications
Artificial Intelligence
topic disease identification
multispectral
fusarium head blight
CA attention mechanism
image segmentation
Control and Systems Engineering
Information Systems
Aerospace Engineering
Computer Science Applications
Artificial Intelligence
description Dong, P., Wang, M., Li, K., Qiao, H., Zhao, Y., Bação, F., Shi, L., Guo, W., & Si, H. (2024). Research on the Identification of Wheat Fusarium Head Blight Based on Multispectral Remote Sensing from UAVs. Drones, 8(9), 1-18. Article 445. https://doi.org/10.3390/drones8090445 --- This research was funded by the Key Research and Development Project of Henan Province, China (241111110800); Natural Science Foundation of Henan Province, China (232300420186); Key Scientific and Technological Project of Henan Province (242102111193); National Natural Science Foundation of China (32271993); Joint Fund of Science and Technology Research Development program (Cultivation project of preponderant discipline) of Henan Province, China (222301420113, 222301420114); Key Research and Development Project of Henan Province, China (231111110100); and Henan Center for Outstanding Overseas Scientists (Project No. GZS2024006). FCT (Fundação para a Ciência e a Tecnologia), under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS).
publishDate 2024
dc.date.none.fl_str_mv 2024-09-10T22:22:01Z
2024-08-30
2024-08-30T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/171517
url http://hdl.handle.net/10362/171517
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2504-446X
PURE: 99262757
https://doi.org/10.3390/drones8090445
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 18
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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