Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features
Leaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimatin...
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MDPI AG
2025-03-01
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| Series: | Plants |
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| author | Yuchen Wang Jianliang Wang Jiayue Li Jiacheng Wang Hanzeyu Xu Tao Liu Juan Wang |
| author_facet | Yuchen Wang Jianliang Wang Jiayue Li Jiacheng Wang Hanzeyu Xu Tao Liu Juan Wang |
| author_sort | Yuchen Wang |
| collection | DOAJ |
| description | Leaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimating maize LWC utilizing UAV-based multispectral imagery combined with a Random Forest Regression (RFR) model. By extracting vegetation indices, image coverage, and texture features and integrating them with ground-truth data, the study examines the variation in LWC estimation accuracy across different growth stages. The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. Moreover, the RFR model consistently outperforms multiple linear regression (MLR) and ridge regression (RR) models throughout the growing season, demonstrating lower errors on both training and testing datasets. Notably, the RFR model exhibits significantly reduced errors in the training dataset compared to both MLR and RR models. Following particle swarm optimization (PSO), the prediction accuracy of the RFR model is notably enhanced, with the RRMSE on the training dataset decreasing from 1.46% to 1.19%. This study provides an effective approach for estimating maize LWC across different growth stages, supporting crop water management and precision agriculture, and offering valuable insights for the estimation of water content in other crops. |
| format | Article |
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| issn | 2223-7747 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-43fd7593ec8d4af4bda9050a05abafb92025-08-20T02:42:25ZengMDPI AGPlants2223-77472025-03-0114697310.3390/plants14060973Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image FeaturesYuchen Wang0Jianliang Wang1Jiayue Li2Jiacheng Wang3Hanzeyu Xu4Tao Liu5Juan Wang6College of Hydraaulic Science and Engineering, Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaCollege of Hydraaulic Science and Engineering, Yangzhou University, Yangzhou 225009, ChinaLeaf water content (LWC) is a key physiological parameter for assessing maize moisture status, with direct implications for crop growth and yield. Accurate LWC estimation is essential for water resource management and precision agriculture. This study introduces a high-precision method for estimating maize LWC utilizing UAV-based multispectral imagery combined with a Random Forest Regression (RFR) model. By extracting vegetation indices, image coverage, and texture features and integrating them with ground-truth data, the study examines the variation in LWC estimation accuracy across different growth stages. The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. Moreover, the RFR model consistently outperforms multiple linear regression (MLR) and ridge regression (RR) models throughout the growing season, demonstrating lower errors on both training and testing datasets. Notably, the RFR model exhibits significantly reduced errors in the training dataset compared to both MLR and RR models. Following particle swarm optimization (PSO), the prediction accuracy of the RFR model is notably enhanced, with the RRMSE on the training dataset decreasing from 1.46% to 1.19%. This study provides an effective approach for estimating maize LWC across different growth stages, supporting crop water management and precision agriculture, and offering valuable insights for the estimation of water content in other crops.https://www.mdpi.com/2223-7747/14/6/973UAVmultispectral imagemachine learningwheatleaf water content |
| spellingShingle | Yuchen Wang Jianliang Wang Jiayue Li Jiacheng Wang Hanzeyu Xu Tao Liu Juan Wang Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features Plants UAV multispectral image machine learning wheat leaf water content |
| title | Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features |
| title_full | Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features |
| title_fullStr | Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features |
| title_full_unstemmed | Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features |
| title_short | Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features |
| title_sort | estimating maize leaf water content using machine learning with diverse multispectral image features |
| topic | UAV multispectral image machine learning wheat leaf water content |
| url | https://www.mdpi.com/2223-7747/14/6/973 |
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