Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms
文献类型：期刊论文
| 作者 | Hasan, Umut1; Jia, Kai7; Wang, Li7; Wang, Chongyang7; Shen, Ziqi2; Yu, Wenjie3; Sun, Yishan7; Jiang, Hao7; Zhang, Zhicong1; Guo, Jinfeng1 |
| 刊名 | PLANTS-BASEL
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 12期号:3页码:501 |
| 关键词 | litchi leaf chlorophyll content variable selection machine learning |
| DOI | 10.3390/plants12030501 |
| 文献子类 | Article |
| 英文摘要 | The accurate estimation of leaf chlorophyll content (LCC) is a significant foundation in assessing litchi photosynthetic activity and possible nutrient status. Hyperspectral remote sensing data have been widely used in agricultural quantitative monitoring research for the non-destructive assessment of LCC. Variable selection approaches are crucial for analyzing high-dimensional datasets due to the high danger of overfitting, time-intensiveness, or substantial computational requirements. In this study, the performance of five machine learning regression algorithms (MLRAs) was investigated based on the hyperspectral fractional order derivative (FOD) reflection of 298 leaves together with the variable combination population analysis (VCPA)-genetic algorithm (GA) hybrid strategy in estimating the LCC of Litchi. The results showed that the correlation coefficient (r) between the 0.8-order derivative spectrum and LCC had the highest correlation coefficients (r = 0.9179, p < 0.01). The VCPA-GA hybrid strategy fully utilizes VCPA and GA while compensating for their limitations based on a large number of variables. Moreover, the model was developed using the selected 14 sensitive bands from 0.8-order hyperspectral reflectance data with the lowest root mean square error in prediction (RMSEP = 5.04 mu gmiddotcm(-2)). Compared with the five MLRAs, validation results confirmed that the ridge regression (RR) algorithm derived from the 0.2 order was the most effective for estimating the LCC with the coefficient of determination (R2 = 0.88), mean absolute error (MAE = 3.40 mu gmiddotcm(-2)), root mean square error (RMSE = 4.23 mu gmiddotcm-(2)), and ratio of performance to inter-quartile distance (RPIQ = 3.59). This study indicates that a hybrid variable selection strategy (VCPA-GA) and MLRAs are very effective in retrieving the LCC through hyperspectral reflectance at the leaf scale. The proposed methods could further provide some scientific basis for the hyperspectral remote sensing band setting of different platforms, such as an unmanned aerial vehicle (UAV) and satellite. |
| WOS关键词 | WAVELENGTH INTERVAL SELECTION ; VARIABLE SELECTION ; MULTIVARIATE CALIBRATION ; POPULATION ANALYSIS ; REMOTE ESTIMATION ; NIR ; REFLECTANCE ; MODEL ; OPTIMIZES ; NITROGEN |
| WOS研究方向 | Plant Sciences |
| WOS记录号 | WOS:000932976300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200771]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Yili Normal Univ, Inst Resources & Ecol, Yining 835000, Peoples R China 2.Guangdong Acad Sci, Guangzhou Inst Geog, Res Ctr Guangdong Prov Engn Technol Applicat Remot, Guangdong Open Lab Geospatial Informat Technol & A, Guangzhou 510070, Peoples R China 3.Guangzhou Climate & Agrometeorol Ctr, Guangzhou 510070, Peoples R China 4.Maoming Meteorol Observ Guangdong Prov, Maoming 525000, Peoples R China 5.Shenzhen Polytech, Sch Artificial Intelligence, Shenzhen 518055, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 7.Yili Normal Univ, Coll Biol & Geog Sci, Yining 835000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hasan, Umut,Jia, Kai,Wang, Li,et al. Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms[J]. PLANTS-BASEL,2023,12(3):501. |
| APA | Hasan, Umut.,Jia, Kai.,Wang, Li.,Wang, Chongyang.,Shen, Ziqi.,...&Li, Dan.(2023).Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms.PLANTS-BASEL,12(3),501. |
| MLA | Hasan, Umut,et al."Retrieval of Leaf Chlorophyll Contents (LCCs) in Litchi Based on Fractional Order Derivatives and VCPA-GA-ML Algorithms".PLANTS-BASEL 12.3(2023):501. |
入库方式： OAI收割
来源：地理科学与资源研究所
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