A comparison of protocols for high-throughput weeds mapping
Increasing herbicide resistance in the US demands novel approaches to integrated weed management, including targeted chemical use and non-chemical methods. More targeted chemical applications and non-chemical alternative methods help expose weeds to multiple modes of action, decreasing the formation...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003090 |
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| author | Joaquin J. Casanova Nicolas T. Bergmann Jessica E.R. Kalin Garett C. Heineck Ian C. Burke |
| author_facet | Joaquin J. Casanova Nicolas T. Bergmann Jessica E.R. Kalin Garett C. Heineck Ian C. Burke |
| author_sort | Joaquin J. Casanova |
| collection | DOAJ |
| description | Increasing herbicide resistance in the US demands novel approaches to integrated weed management, including targeted chemical use and non-chemical methods. More targeted chemical applications and non-chemical alternative methods help expose weeds to multiple modes of action, decreasing the formation of resistant populations. However, generating prescription maps and evaluating non-chemical methods require field-scale mapping of weeds. Typical methods for weeds mapping either involve laborious mapping on the ground or impractical low-altitude UAV imaging. Additionally, the literature describes an array of imaging techniques demonstrated in very select circumstances. To give clear guidelines for future research, this paper compares three imaging techniques, two weed count model types, and two ground validation methods (quadrat counts and seedbank counts) for remote weeds mapping on five sites experiencing infestations of different common weed species. Overall, the multispectral imaging techniques using Poisson count models and weed counts in quadrats as ground truth outperformed other methods and can be recommended as a pipeline for rapid mapping weeds in field crops. However, though seedbank density did not map well when using imagery, 50 seedbank samples were adequate for assessing seedbank. |
| format | Article |
| id | doaj-art-7cecc3a2adf94e3b8cf37eaa365c33b5 |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-7cecc3a2adf94e3b8cf37eaa365c33b52025-08-20T02:35:46ZengElsevierSmart Agricultural Technology2772-37552025-12-011210107610.1016/j.atech.2025.101076A comparison of protocols for high-throughput weeds mappingJoaquin J. Casanova0Nicolas T. Bergmann1Jessica E.R. Kalin2Garett C. Heineck3Ian C. Burke4USDA ARS, Pullman, WA, 99164, USAWSU Department of Crop and Soil Science, Pullman, WA, 99164, USAWSU Department of Crop and Soil Science, Pullman, WA, 99164, USAUSDA ARS, Prosser, WA, 99350, USAWSU Department of Crop and Soil Science, Pullman, WA, 99164, USAIncreasing herbicide resistance in the US demands novel approaches to integrated weed management, including targeted chemical use and non-chemical methods. More targeted chemical applications and non-chemical alternative methods help expose weeds to multiple modes of action, decreasing the formation of resistant populations. However, generating prescription maps and evaluating non-chemical methods require field-scale mapping of weeds. Typical methods for weeds mapping either involve laborious mapping on the ground or impractical low-altitude UAV imaging. Additionally, the literature describes an array of imaging techniques demonstrated in very select circumstances. To give clear guidelines for future research, this paper compares three imaging techniques, two weed count model types, and two ground validation methods (quadrat counts and seedbank counts) for remote weeds mapping on five sites experiencing infestations of different common weed species. Overall, the multispectral imaging techniques using Poisson count models and weed counts in quadrats as ground truth outperformed other methods and can be recommended as a pipeline for rapid mapping weeds in field crops. However, though seedbank density did not map well when using imagery, 50 seedbank samples were adequate for assessing seedbank.http://www.sciencedirect.com/science/article/pii/S2772375525003090WeedsRemote sensingUAVComputer vision |
| spellingShingle | Joaquin J. Casanova Nicolas T. Bergmann Jessica E.R. Kalin Garett C. Heineck Ian C. Burke A comparison of protocols for high-throughput weeds mapping Smart Agricultural Technology Weeds Remote sensing UAV Computer vision |
| title | A comparison of protocols for high-throughput weeds mapping |
| title_full | A comparison of protocols for high-throughput weeds mapping |
| title_fullStr | A comparison of protocols for high-throughput weeds mapping |
| title_full_unstemmed | A comparison of protocols for high-throughput weeds mapping |
| title_short | A comparison of protocols for high-throughput weeds mapping |
| title_sort | comparison of protocols for high throughput weeds mapping |
| topic | Weeds Remote sensing UAV Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003090 |
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