Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality
Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus,...
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MDPI AG
2025-03-01
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| author | Lorena N. Lacerda Matheus Ardigueri Thiago O. C. Barboza John Snider Devendra P. Chalise Stefano Gobbo George Vellidis |
| author_facet | Lorena N. Lacerda Matheus Ardigueri Thiago O. C. Barboza John Snider Devendra P. Chalise Stefano Gobbo George Vellidis |
| author_sort | Lorena N. Lacerda |
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| description | Estimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R<sup>2</sup> of 0.62, and for LAI using MSR and NDVI an R<sup>2</sup> of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions. |
| format | Article |
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| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Agronomy |
| spelling | doaj-art-cd466d862da44ca1977657cd36bf4f4c2025-08-20T03:40:42ZengMDPI AGAgronomy2073-43952025-03-0115369210.3390/agronomy15030692Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and QualityLorena N. Lacerda0Matheus Ardigueri1Thiago O. C. Barboza2John Snider3Devendra P. Chalise4Stefano Gobbo5George Vellidis6Crop and Soil Sciences Department, University of Georgia, Athens, GA 30602, USADepartment of Agriculture, School of Agriculture, Federal University of Lavras, Lavras 37200-000, MG, BrazilDepartment of Agriculture, School of Agriculture, Federal University of Lavras, Lavras 37200-000, MG, BrazilCrop and Soil Sciences Department, University of Georgia, Tifton, GA 31793, USADepartment of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USADepartment of Agronomy, Food, and Natural Resources, Animals and the Environment, University of Padova, 35020 Legnaro, ItalyCrop and Soil Sciences Department, University of Georgia, Tifton, GA 31793, USAEstimating cotton fiber quality early in the season, or its field variability, is impractical due to limitations in current methods, and it has not been widely explored. Similarly, few studies have tried estimating the parameters contributing to in-season cotton yield using UAV-based sensors. Thus, this study aims to explore the potential of using UAV-based multispectral images to estimate important in-season parameters, such as intercepted photosynthetically active radiation (IPAR), cotton height, the number of mainstem nodes, leaf area index (LAI), and end-of-the-season yield and cotton fiber quality parameters. Research trials were carried out in 2018 and 2020 in two experimental fields. In both years, a randomized complete block design was used with three cotton cultivars (2018), three plant growth regulators (2020), and three different irrigation levels to promote variability (both years). Cotton growth parameters were collected throughout the season on the same dates as UAV flights. Yield and fiber quality data were collected during harvest. The VI-based models used in this study were mostly sensitive to differences in cotton growth and final yield but less sensitive in detecting variation in cotton fiber quality indicators, such as length, strength, and micronaire, early in the season. The best performing regression model among the three fiber quality indicators was achieved in 2020, using a combination of four VIs, which explained 68% of the micronaire variability at 71 DAP. Results from this study also showed that multispectral-based VIs can be applied as early as the squaring stage at around 44 DAP to estimate most cotton growth indicators and final lint yield. Multiple linear regression validation models for height using NDVI, GNDVI, and RDVI obtained an R<sup>2</sup> of 0.62, and for LAI using MSR and NDVI an R<sup>2</sup> of 0.60. For lint yield, the best regression model combined four VIs and explained 66% of the yield variability. The ability to capture the variability in important growth and yield parameters early in the season can provide useful insights on potential crop performance and aid in in-season decisions.https://www.mdpi.com/2073-4395/15/3/692vegetation indicesIPARcotton yieldLAImultispectral imagesUAV |
| spellingShingle | Lorena N. Lacerda Matheus Ardigueri Thiago O. C. Barboza John Snider Devendra P. Chalise Stefano Gobbo George Vellidis Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality Agronomy vegetation indices IPAR cotton yield LAI multispectral images UAV |
| title | Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality |
| title_full | Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality |
| title_fullStr | Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality |
| title_full_unstemmed | Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality |
| title_short | Using High-Resolution Multispectral Data to Evaluate In-Season Cotton Growth Parameters and End-of-the-Season Cotton Fiber Yield and Quality |
| title_sort | using high resolution multispectral data to evaluate in season cotton growth parameters and end of the season cotton fiber yield and quality |
| topic | vegetation indices IPAR cotton yield LAI multispectral images UAV |
| url | https://www.mdpi.com/2073-4395/15/3/692 |
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