Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability
Many important ecological processes play out over large geographic ranges, and accurate large-scale monitoring of populations is a requirement for their effective management. Of particular interest are agricultural weeds, which cause widespread economic and ecological damage. However, the scale of w...
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| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4749 |
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| author | Robert M. Goodsell Shaun Coutts William Oxford Helen Hicks David Comont Robert P. Freckleton Dylan Z. Childs |
| author_facet | Robert M. Goodsell Shaun Coutts William Oxford Helen Hicks David Comont Robert P. Freckleton Dylan Z. Childs |
| author_sort | Robert M. Goodsell |
| collection | DOAJ |
| description | Many important ecological processes play out over large geographic ranges, and accurate large-scale monitoring of populations is a requirement for their effective management. Of particular interest are agricultural weeds, which cause widespread economic and ecological damage. However, the scale of weed population data collection is limited by an inevitable trade-off between quantity and quality. Remote sensing offers a promising route to the large-scale collection of population state data. However, a key challenge is to collect high enough resolution data and account for between-site variability in environmental (i.e., radiometric) conditions that may make prediction of population states in new data challenging. Here, we use a multi-site hyperspectral image dataset in conjunction with ensemble learning techniques in an attempt to predict densities of an arable weed (<i>Alopecurus myosuroides</i>, Huds) across an agricultural landscape. We demonstrate reasonable predictive performance (using the geometric mean score-GMS) when classifiers are used to predict new data from the same site (GMS = 0.74-low density, GMS = 0.74-medium density, GMS = 0.7-High density). However, even using flexible ensemble techniques to account for variability in spectral data, we show that out-of-field predictive performance is poor (GMS = 0.06-low density, GMS = 0.13-medium density, GMS = 0.08-High density). This study highlights the difficulties in identifying weeds in situ, even using high quality image data from remote sensing. |
| format | Article |
| id | doaj-art-349c3a9c678f4707a14ad6bd6a72266f |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-349c3a9c678f4707a14ad6bd6a72266f2024-12-27T14:51:05ZengMDPI AGRemote Sensing2072-42922024-12-011624474910.3390/rs16244749Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site VariabilityRobert M. Goodsell0Shaun Coutts1William Oxford2Helen Hicks3David Comont4Robert P. Freckleton5Dylan Z. Childs6Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield S10 2TN, UKLincoln Institute for Agri-Food Technology, College of Science, Lincoln LN2 2BJ, UK2Excel Aviation Ltd., Hangar 3, Fourth Avenue, Doncaster DN9 3GE, UKSchool of Animal Rural & Environmental Sciences, Nottingham Trent University, 50 Shakespeare St., Nottingham NG1 4FQ, UKRothamsted Research, Harpenden, Hertfordshire AL5 2JQ, UKEcology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield S10 2TN, UKEcology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield S10 2TN, UKMany important ecological processes play out over large geographic ranges, and accurate large-scale monitoring of populations is a requirement for their effective management. Of particular interest are agricultural weeds, which cause widespread economic and ecological damage. However, the scale of weed population data collection is limited by an inevitable trade-off between quantity and quality. Remote sensing offers a promising route to the large-scale collection of population state data. However, a key challenge is to collect high enough resolution data and account for between-site variability in environmental (i.e., radiometric) conditions that may make prediction of population states in new data challenging. Here, we use a multi-site hyperspectral image dataset in conjunction with ensemble learning techniques in an attempt to predict densities of an arable weed (<i>Alopecurus myosuroides</i>, Huds) across an agricultural landscape. We demonstrate reasonable predictive performance (using the geometric mean score-GMS) when classifiers are used to predict new data from the same site (GMS = 0.74-low density, GMS = 0.74-medium density, GMS = 0.7-High density). However, even using flexible ensemble techniques to account for variability in spectral data, we show that out-of-field predictive performance is poor (GMS = 0.06-low density, GMS = 0.13-medium density, GMS = 0.08-High density). This study highlights the difficulties in identifying weeds in situ, even using high quality image data from remote sensing.https://www.mdpi.com/2072-4292/16/24/4749black-grassmachine learningweedshyperspectral imagery |
| spellingShingle | Robert M. Goodsell Shaun Coutts William Oxford Helen Hicks David Comont Robert P. Freckleton Dylan Z. Childs Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability Remote Sensing black-grass machine learning weeds hyperspectral imagery |
| title | Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability |
| title_full | Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability |
| title_fullStr | Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability |
| title_full_unstemmed | Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability |
| title_short | Black-Grass Monitoring Using Hyperspectral Image Data Is Limited by Between-Site Variability |
| title_sort | black grass monitoring using hyperspectral image data is limited by between site variability |
| topic | black-grass machine learning weeds hyperspectral imagery |
| url | https://www.mdpi.com/2072-4292/16/24/4749 |
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