Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat
Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents....
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MDPI AG
2024-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/18/3446 |
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| author | Frank Gyan Okyere Daniel Kingsley Cudjoe Nicolas Virlet March Castle Andrew Bernard Riche Latifa Greche Fady Mohareb Daniel Simms Manal Mhada Malcolm John Hawkesford |
| author_facet | Frank Gyan Okyere Daniel Kingsley Cudjoe Nicolas Virlet March Castle Andrew Bernard Riche Latifa Greche Fady Mohareb Daniel Simms Manal Mhada Malcolm John Hawkesford |
| author_sort | Frank Gyan Okyere |
| collection | DOAJ |
| description | Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (P<sub>n</sub>) and stomatal conductance (g<sub>s</sub>) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast g<sub>s</sub> and P<sub>n</sub> and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions. |
| format | Article |
| id | doaj-art-ce59b9a504b14cdbaa1d7bc9f86708c5 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ce59b9a504b14cdbaa1d7bc9f86708c52025-08-20T01:55:49ZengMDPI AGRemote Sensing2072-42922024-09-011618344610.3390/rs16183446Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in WheatFrank Gyan Okyere0Daniel Kingsley Cudjoe1Nicolas Virlet2March Castle3Andrew Bernard Riche4Latifa Greche5Fady Mohareb6Daniel Simms7Manal Mhada8Malcolm John Hawkesford9Sustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKSustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKSustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKSustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKSustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKSustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKSchool of Water Energy and Environment, Cranfield University, Cranfield MK43 0AL, UKSchool of Water Energy and Environment, Cranfield University, Cranfield MK43 0AL, UKDepartment of AgroBioSciences, University of Mohammed VI Polytechnic, Ben Guerir 43150, MoroccoSustainable Soils and Crops, Rothamsted Research, Harpenden AL5 2JQ, UKAccurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (P<sub>n</sub>) and stomatal conductance (g<sub>s</sub>) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast g<sub>s</sub> and P<sub>n</sub> and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.https://www.mdpi.com/2072-4292/16/18/3446drought stressgas exchange measurementshyperspectral imagingmachine learningvegetation indices |
| spellingShingle | Frank Gyan Okyere Daniel Kingsley Cudjoe Nicolas Virlet March Castle Andrew Bernard Riche Latifa Greche Fady Mohareb Daniel Simms Manal Mhada Malcolm John Hawkesford Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat Remote Sensing drought stress gas exchange measurements hyperspectral imaging machine learning vegetation indices |
| title | Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat |
| title_full | Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat |
| title_fullStr | Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat |
| title_full_unstemmed | Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat |
| title_short | Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat |
| title_sort | hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat |
| topic | drought stress gas exchange measurements hyperspectral imaging machine learning vegetation indices |
| url | https://www.mdpi.com/2072-4292/16/18/3446 |
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