Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach
The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduc...
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/3917 |
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| author | Jun Zhang Jinpeng Cheng Cuiping Liu Qiang Wu Shuping Xiong Hao Yang Shenglong Chang Yuanyuan Fu Mohan Yang Shiyu Zhang Guijun Yang Xinming Ma |
| author_facet | Jun Zhang Jinpeng Cheng Cuiping Liu Qiang Wu Shuping Xiong Hao Yang Shenglong Chang Yuanyuan Fu Mohan Yang Shiyu Zhang Guijun Yang Xinming Ma |
| author_sort | Jun Zhang |
| collection | DOAJ |
| description | The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R<sup>2</sup> values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring. |
| format | Article |
| id | doaj-art-d7ca854d7ea74fec8cc5e1bf6de044b2 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-d7ca854d7ea74fec8cc5e1bf6de044b22024-11-08T14:40:12ZengMDPI AGRemote Sensing2072-42922024-10-011621391710.3390/rs16213917Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection ApproachJun Zhang0Jinpeng Cheng1Cuiping Liu2Qiang Wu3Shuping Xiong4Hao Yang5Shenglong Chang6Yuanyuan Fu7Mohan Yang8Shiyu Zhang9Guijun Yang10Xinming Ma11College of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaCollege of Software, Henan Normal University, Xinxiang 453000, ChinaCollege of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaKey Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, ChinaCollege of Agronomy, Henan Agricultural University, Zhengzhou 450046, ChinaThe Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R<sup>2</sup> values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.https://www.mdpi.com/2072-4292/16/21/3917leaf area indexhyperparameter optimizationBayesian algorithmrandom forests |
| spellingShingle | Jun Zhang Jinpeng Cheng Cuiping Liu Qiang Wu Shuping Xiong Hao Yang Shenglong Chang Yuanyuan Fu Mohan Yang Shiyu Zhang Guijun Yang Xinming Ma Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach Remote Sensing leaf area index hyperparameter optimization Bayesian algorithm random forests |
| title | Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach |
| title_full | Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach |
| title_fullStr | Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach |
| title_full_unstemmed | Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach |
| title_short | Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach |
| title_sort | enhanced crop leaf area index estimation via random forest regression bayesian optimization and feature selection approach |
| topic | leaf area index hyperparameter optimization Bayesian algorithm random forests |
| url | https://www.mdpi.com/2072-4292/16/21/3917 |
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