| 基于耦合算法的花生叶片光合色素含量反演模型 |
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| 引用本文： | 刘欣蓓,苏涛,雷波,朱菲,邸俊楠,孟成,徐良泉,王仁义.基于耦合算法的花生叶片光合色素含量反演模型[J].农业工程学报,2023,39(16):198-207. |
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| 作者姓名： | 刘欣蓓 苏涛 雷波 朱菲 邸俊楠 孟成 徐良泉 王仁义 |
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| 作者单位： | 安徽理工大学空间信息与测绘工程学院，淮南 232001;中国水利水电科学研究院水利研究所，北京 100048 |
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| 基金项目： | 安徽理工大学引进人才科研启动项目（ZY030）；国家重点研发计划（2018YFC0407703） |
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| 摘 要： | 准确获取及预测光合色素含量可为精细化种植管理提供数据依据,为探究花生冠层叶片色素吸收特征,该研究以开花下针期的花生冠层叶片为研究对象,以ASD Field Spec4野外便携式高光谱仪采集的光谱数据为数据源,进行花生叶片叶绿素含量和类胡萝卜素含量反演。通过对比7种单一筛选特征波长变量算法及结合4种模型(PLSR、SVR、GBDT和XGBoost)的结果,优选出3种算法进行两两耦合。结果表明:1)在单一算法试验中IRIV、UVE和GA算法结果较优;2)在耦合算法试验中UVE-IRIV、GA-IRIV和GA-UVE方法都能有效降维,且模型稳定性提升。在叶绿素含量反演模型中,GA-IRIV-XGBoost模型精度最高,R2=0.622,RMSE=0.235 mg/g;在类胡萝卜素含量反演模型中,UVE-IRIV-XGBoost模型精度最高,R2=0.575,RMSE=0.056 mg/g;3)比较两种色素反演模型的预测精度,表明叶绿素的预测精度优于类胡萝卜素。该结果可为快速、准确预测花生叶片光合色素含量提供一种方法。
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| 关 键 词： | 作物 高光谱 花生叶片 光合色素 特征波长变量 耦合算法 XGBoost |
| 收稿时间： | 2023/3/10 0:00:00 |
| 修稿时间： | 2023/8/9 0:00:00 |
Inverse model for the photosynthetic pigment content of peanut leaves using coupling algorithm |
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| LIU Xinbei,SU Tao,LEI Bo,ZHU Fei,DI Junnan,MENG Cheng,XU Liangquan,WANG Renyi.Inverse model for the photosynthetic pigment content of peanut leaves using coupling algorithm[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(16):198-207. |
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| Authors: | LIU Xinbei SU Tao LEI Bo ZHU Fei DI Junnan MENG Cheng XU Liangquan WANG Renyi |
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| Institution: | School of Spatial Information and Geomatics Engineering, Anhui University of Science and Technology, Huainan 232001, China;Department of Irrigation and Drainage, China Institute of WaterResources and Hydropower Research, Beijing 100048, China |
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| Abstract: | Peanut is one of the most important economic and oilseed crops in world. It is vital to ensure the stability of the production for national oil security, due to rich in oil, protein, dietary fiber, and micronutrients with multiple functional components and very high nutritional value. Furthermore, pigments (such as the chlorophyll and carotenoids) are greatly contributed to the photosynthesis in plants. Among them, the content of chlorophyll is very closely related to the photosynthetic capacity, growth and development, as well as nutritional status of vegetation, representing the stress, growth and senescence symptoms. Carotenoids can absorb and transfer the solar radiant energy, and also dissipate the excess energy to protect the photosynthetic system, particularly for the exposure to strong sunlight. It is a high demand for the accurate acquisition and prediction of photosynthetic pigment content for fine planting management. Hyperspectral technology has been widely used for the rapid detection of crop physiological indicators and growth in recent years, due to its high efficiency and non-destructive means. In this study, an inversion model was established for the photosynthetic pigment content of peanut leaf using coupling algorithm. The research object was taken as the peanut canopy leaves at the flowering hypocotyl stage. The data source was collected by ASD Field Spec4 field portable hyperspectrometer. A systematic investigation was also conducted to determine the photosynthetic pigment uptake of peanut canopy leaves. The raw spectral data was preprocessed using Savitzky-Golay smoothing (SG) combined with the standard normal variables (SNV). Seven single screening algorithms of feature wavelength selection were compared, including the correlation coefficient analysis (CC), successive projections algorithm (SPA), random frog (RF), iteratively retains informative variables (IRIV), uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and genetic algorithm (GA). Four models were combined with the partial least squares regression (PLSR), support vector regression (SVR), gradient boosting decision tree (GBDT), and eXtreme gradient boosting (XGBoost). Full wavelength models were used for the comparative analysis, where the evaluation indexes were used as the determination of coefficients (R2) and root mean square error (RMSE). Three optimal algorithms were selected for pairwise coupling. The results showed that (1) In the single algorithm experiments, the superior performance was achieved in the UVE, IRIV and GA. Specifically, the R2 reached 0.591, and 0.565 in the prediction model of chlorophyll and carotenoid content, respectively. There was no significant improvement in the accuracy in the CC, RF, SPA, and CARS. (2) In the coupled algorithm experiments, the UVE-IRIV, GA-IRIV, and GA-UVE were greatly improved the prediction accuracy, while effectively simplified the modeling complexity. Among them, the GA-IRIV-XGBoost inversion model of chlorophyll content reached the highest accuracy with R2=0.622 and RMSE=0.235, whereas, the UVE-IRIV-XGBoost inversion model of carotenoid content reached the highest accuracy with R2=0.575 and RMSE=0.056. (3) The prediction accuracy of chlorophyll was better than that of carotenoids. In summary, the coupling algorithm can effectively compress the variables for the simplified model with the improved robustness. A rapid, nondestructive and accurate prediction was achieved in the photosynthetic pigment content in peanut leaves. The better effect was coupled for the high accuracy and stability of the improved model using the variable screening. The structure of improved model was also simplified to extract a small number of effective information variables. The finding can provide the promising data base to detect the photosynthetic pigment content of peanut leaves for the yield estimation of peanuts. |
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| Keywords: | crops hyperspectral peanut leaves photosynthetic pigment characteristic wavelength variables coupling algorithm XGBoost |
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