Evaluation of Crop Health Status With UAS Multispectral Imagery
This study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an unmanned aircraft system (UAS), and the crop health status was assessed through the green ar...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9633165/ |
| _version_ | 1798034767769239552 |
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| author | Odysseas Vlachopoulos Brigitte Leblon Jinfei Wang Ataollah Haddadi Armand LaRocque Greg Patterson |
| author_facet | Odysseas Vlachopoulos Brigitte Leblon Jinfei Wang Ataollah Haddadi Armand LaRocque Greg Patterson |
| author_sort | Odysseas Vlachopoulos |
| collection | DOAJ |
| description | This study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an unmanned aircraft system (UAS), and the crop health status was assessed through the green area index (GAI) and vegetation indices (VIs). GAI maps were produced from the UAS imagery and VIs used machine learning pipelines with several regression algorithms (multiple linear models, support vector machines, random forests, and artificial neural networks) along with a feature selection strategy. The random forests algorithm was shown to be the best algorithm for GAI prediction with an average relative root mean square error of 10.86% and a mean absolute error of 0.67. The resulting GAI maps and the regression feature space were classified with random forests to discriminate between vigorous and stressed crop areas. We achieved a mean overall accuracy of 94%. The limits of the study are also presented. |
| first_indexed | 2024-04-11T20:48:54Z |
| format | Article |
| id | doaj.art-1db096eab6ba49f1b91832224d112ec8 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T20:48:54Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-1db096eab6ba49f1b91832224d112ec82022-12-22T04:03:54ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-011529730810.1109/JSTARS.2021.31322289633165Evaluation of Crop Health Status With UAS Multispectral ImageryOdysseas Vlachopoulos0https://orcid.org/0000-0002-5407-3024Brigitte Leblon1Jinfei Wang2https://orcid.org/0000-0002-8404-0530Ataollah Haddadi3Armand LaRocque4Greg Patterson5Faculty of Forestry and Environmental Management, University of New Brunswick, London, NB, CanadaFaculty of Forestry and Environmental Management, University of New Brunswick, London, NB, CanadaDepartment of Geography and Environment, University of Western Ontario, London, ON, CanadaA&L Canada Laboratories, London, ON, CanadaFaculty of Forestry and Environmental Management, University of New Brunswick, London, NB, CanadaA&L Canada Laboratories, London, ON, CanadaThis study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an unmanned aircraft system (UAS), and the crop health status was assessed through the green area index (GAI) and vegetation indices (VIs). GAI maps were produced from the UAS imagery and VIs used machine learning pipelines with several regression algorithms (multiple linear models, support vector machines, random forests, and artificial neural networks) along with a feature selection strategy. The random forests algorithm was shown to be the best algorithm for GAI prediction with an average relative root mean square error of 10.86% and a mean absolute error of 0.67. The resulting GAI maps and the regression feature space were classified with random forests to discriminate between vigorous and stressed crop areas. We achieved a mean overall accuracy of 94%. The limits of the study are also presented.https://ieeexplore.ieee.org/document/9633165/Cropgreen area index (GAI)leaf area index (LAI)machine learningmultispectralprecision agriculture |
| spellingShingle | Odysseas Vlachopoulos Brigitte Leblon Jinfei Wang Ataollah Haddadi Armand LaRocque Greg Patterson Evaluation of Crop Health Status With UAS Multispectral Imagery IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Crop green area index (GAI) leaf area index (LAI) machine learning multispectral precision agriculture |
| title | Evaluation of Crop Health Status With UAS Multispectral Imagery |
| title_full | Evaluation of Crop Health Status With UAS Multispectral Imagery |
| title_fullStr | Evaluation of Crop Health Status With UAS Multispectral Imagery |
| title_full_unstemmed | Evaluation of Crop Health Status With UAS Multispectral Imagery |
| title_short | Evaluation of Crop Health Status With UAS Multispectral Imagery |
| title_sort | evaluation of crop health status with uas multispectral imagery |
| topic | Crop green area index (GAI) leaf area index (LAI) machine learning multispectral precision agriculture |
| url | https://ieeexplore.ieee.org/document/9633165/ |
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