UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation
Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) v...
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/19/4658 |
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| author | Qi Wang Xiaokai Chen Huayi Meng Huiling Miao Shiyu Jiang Qingrui Chang |
| author_facet | Qi Wang Xiaokai Chen Huayi Meng Huiling Miao Shiyu Jiang Qingrui Chang |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Chlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values. |
| first_indexed | 2024-03-10T21:36:57Z |
| format | Article |
| id | doaj.art-855e83725e1248f887fbb89bfca560ec |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T21:36:57Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-855e83725e1248f887fbb89bfca560ec2023-11-19T14:58:08ZengMDPI AGRemote Sensing2072-42922023-09-011519465810.3390/rs15194658UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values EstimationQi Wang0Xiaokai Chen1Huayi Meng2Huiling Miao3Shiyu Jiang4Qingrui Chang5College of Nature Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCollege of Nature Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCollege of Nature Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCollege of Nature Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCollege of Nature Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaCollege of Nature Resources and Environment, Northwest A&F University, Yangling, Xianyang 712100, ChinaChlorophyll is an important indicator for monitoring crop growth and is vital for agricultural management. Therefore, rapid and accurate estimation of chlorophyll content is important for decision support in precision agriculture to accurately monitor the SPAD (Soil and Plant Analyzer Development) values of winter wheat. This study used winter wheat to obtain canopy reflectance based on UAV hyperspectral data and to calculate different vegetation indices and red-edge parameters. The best-performing vegetation indices and red-edge parameters were selected by Pearson correlation analysis and multiple stepwise regression (MSR). SPAD values were estimated using a combination of vegetation indices, vegetation indices and red-edge parameters as model factors, two types of machine learning (ML), a support vector machine (SVM), and a backward propagation neural network (BPNN), and partial least squares regression (PLSR) for four growth stages of winter wheat, and validated using independent samples. The results show that for the same data source, the best vegetation indices or red-edge parameters for estimating SPAD values differed at different growth stages and that combining vegetation indices with red-edge parameters gave better estimates than using only vegetation indices as an input factor for estimating SPAD values. There is no significant difference between PLSR, SVM, and BPNN methods in estimating SPAD values, with better stability of the estimated models using machine learning methods. Different growth stages have a large impact on winter wheat SPAD values estimates, with the accuracy of the four growth stage models increasing in the following order: booting < heading < filling < flowering. This study shows that using a combination of vegetation indices and red-edge parameters can improve SPAD values estimates compared to using vegetation indices alone. In the future, the choice of appropriate factors and methods will need to be considered when constructing models to estimate crop SPAD values.https://www.mdpi.com/2072-4292/15/19/4658UAV hyperspectralSPAD valuesvegetation indexred-edge parametersmachine learning |
| spellingShingle | Qi Wang Xiaokai Chen Huayi Meng Huiling Miao Shiyu Jiang Qingrui Chang UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation Remote Sensing UAV hyperspectral SPAD values vegetation index red-edge parameters machine learning |
| title | UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation |
| title_full | UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation |
| title_fullStr | UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation |
| title_full_unstemmed | UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation |
| title_short | UAV Hyperspectral Data Combined with Machine Learning for Winter Wheat Canopy SPAD Values Estimation |
| title_sort | uav hyperspectral data combined with machine learning for winter wheat canopy spad values estimation |
| topic | UAV hyperspectral SPAD values vegetation index red-edge parameters machine learning |
| url | https://www.mdpi.com/2072-4292/15/19/4658 |
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