The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia
Abstract Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, S...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43770-y |
| _version_ | 1797452802029518848 |
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| author | Gerald Blasch Tadesse Anberbir Tamirat Negash Lidiya Tilahun Fikrte Yirga Belayineh Yoseph Alemayehu Girma Mamo David P. Hodson Francelino A. Rodrigues |
| author_facet | Gerald Blasch Tadesse Anberbir Tamirat Negash Lidiya Tilahun Fikrte Yirga Belayineh Yoseph Alemayehu Girma Mamo David P. Hodson Francelino A. Rodrigues |
| author_sort | Gerald Blasch |
| collection | DOAJ |
| description | Abstract Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages. |
| first_indexed | 2024-03-09T15:13:56Z |
| format | Article |
| id | doaj.art-aaabbc0939574ae3b877c1c17fcdf583 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:13:56Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-aaabbc0939574ae3b877c1c17fcdf5832023-11-26T13:14:15ZengNature PortfolioScientific Reports2045-23222023-10-0113111910.1038/s41598-023-43770-yThe potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in EthiopiaGerald Blasch0Tadesse Anberbir1Tamirat Negash2Lidiya Tilahun3Fikrte Yirga Belayineh4Yoseph Alemayehu5Girma Mamo6David P. Hodson7Francelino A. Rodrigues8International Maize and Wheat Improvement Center (CIMMYT)Ethiopian Institute of Agricultural Research (EIAR)Kulumsa Agricultural Research Center (KARC)Kulumsa Agricultural Research Center (KARC)Kulumsa Agricultural Research Center (KARC)International Maize and Wheat Improvement Center (CIMMYT)Ethiopian Institute of Agricultural Research (EIAR)International Maize and Wheat Improvement Center (CIMMYT)International Maize and Wheat Improvement Center (CIMMYT)Abstract Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.https://doi.org/10.1038/s41598-023-43770-y |
| spellingShingle | Gerald Blasch Tadesse Anberbir Tamirat Negash Lidiya Tilahun Fikrte Yirga Belayineh Yoseph Alemayehu Girma Mamo David P. Hodson Francelino A. Rodrigues The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia Scientific Reports |
| title | The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia |
| title_full | The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia |
| title_fullStr | The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia |
| title_full_unstemmed | The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia |
| title_short | The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia |
| title_sort | potential of uav and very high resolution satellite imagery for yellow and stem rust detection and phenotyping in ethiopia |
| url | https://doi.org/10.1038/s41598-023-43770-y |
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