Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP
Abstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguit...
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Nature Portfolio
2025-03-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84977-x |
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| author | YaFeng Li XinGang Xu WenBiao Wu Yaohui Zhu LuTao Gao XiangTai Jiang Yang Meng GuiJun Yang HanYu Xue |
| author_facet | YaFeng Li XinGang Xu WenBiao Wu Yaohui Zhu LuTao Gao XiangTai Jiang Yang Meng GuiJun Yang HanYu Xue |
| author_sort | YaFeng Li |
| collection | DOAJ |
| description | Abstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson’s algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where $$\:{R}^{2}$$ =0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators. |
| format | Article |
| id | doaj-art-262620650e3f4edea502b3da5de2b27b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-262620650e3f4edea502b3da5de2b27b2025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115111310.1038/s41598-024-84977-xHyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BPYaFeng Li0XinGang Xu1WenBiao Wu2Yaohui Zhu3LuTao Gao4XiangTai Jiang5Yang Meng6GuiJun Yang7HanYu Xue8Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesSchool of Agricultural Engineering, Jiangsu UniversityCollege of Big Data, Yunnan Agricultural UniversityKey Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesKey Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agricultural and Forestry SciencesAbstract Leaf chlorophyll content (LCC) is a key indicator for assessing the growth of grapes. Hyperspectral techniques have been applied to LCC research. However, quantitative prediction of grape LCC using this technique remains challenging due to baseline drift, spectral peak overlap, and ambiguity in the sensitive spectral range. To address these issues, two typical crop leaf hyperspectral data were collected to reveal the spectral response characteristics of grape LCC using standardization by variables (SNV) and multiple far scattering correction (MSC) preprocessing variations. The sensitive spectral range is determined by Pearson’s algorithm, and sensitive features are further extracted within that range using Extreme Gradient Boosting (XGBoost), Recursive Feature Elimination (RFE), and Principal components analysis (PCA). Comparison of the prediction ability of Random Forest Regression (RFR) algorithm, Support Vector Machine Regression (SVR) model, and Genetic Algorithm-Based Neural Network (GA-BP) on grape LCC based on sensitive features. A SNV-RFE-GA-BP framework for predicting hyperspectral LCC in grapes is proposed, where $$\:{R}^{2}$$ =0.835 and NRMSE = 0.091. The analysis results show that SNV and MSC treatments improve the correlation between spectral reflectance and LCC, and different feature screening methods have a greater impact on the model prediction accuracy. It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. This method provides a new framework theory for constructing a hyperspectral analytical model of grapevine key growth indicators.https://doi.org/10.1038/s41598-024-84977-xData preprocessingFeature selectionMachine learningHyperspectral monitoring. |
| spellingShingle | YaFeng Li XinGang Xu WenBiao Wu Yaohui Zhu LuTao Gao XiangTai Jiang Yang Meng GuiJun Yang HanYu Xue Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP Scientific Reports Data preprocessing Feature selection Machine learning Hyperspectral monitoring. |
| title | Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP |
| title_full | Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP |
| title_fullStr | Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP |
| title_full_unstemmed | Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP |
| title_short | Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP |
| title_sort | hyperspectral estimation of chlorophyll content in grapevine based on feature selection and ga bp |
| topic | Data preprocessing Feature selection Machine learning Hyperspectral monitoring. |
| url | https://doi.org/10.1038/s41598-024-84977-x |
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