| Estimation of kiwifruit leaf chlorophyll content based on Stacking ensemble learning |
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| DOI：10.7606/j.issn.1000-7601.2023.04.26 |
| Key Words: kiwifruit chlorophyll content hyperspectral Stacking ensemble learning |
| Author Name | Affiliation | | FU Xintong | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | CHANG Qingrui | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | ZHANG Youming | Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang, Henan 453002, China | | ZHANG Zijuan | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | ZHENG Zhikang | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | LI Kai | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| Chlorophyll content can effectively represent the intensity of plant photosynthesis which is one of the important parameters reflecting plant growth status. The chlorophyll content and spectral reflectance of kiwifruit leaves at the strong fruit stage at the northern foot of the Qinling Mountains were measured. By analyzing the correlation between hyperspectral parameters and chlorophyll content in the range of 380~1 000 nm, the input characteristics of the estimation model were screened out. Random forest, extreme gradient boosting, K-nearest neighbor, light gradient boosting machine, and ridge regression were selected as the base models and linear regression as the meta model to establish the estimation model of chlorophyll content in kiwifruit leaves based on Stacking ensemble learning. The generalization ability of the model was improved through grid search and 5-fold cross, and the Stacking model was compared with multiple single models. The results showed that: (1) The variation trend of hyperspectral reflectance of kiwifruit leaves with different chlorophyll contents was basically the same, showing the characteristics of “one peak, two valleys and one platform” in the range of 380~1 000 nm. (2) The correlation between hyperspectral parameters and chlorophyll content in kiwifruit leaves was good. In the optimized spectral index and the traditional spectral index, the highest correlation with chlorophyll content was ratio spectral index（RSI′581,438,r=0.947) and red edge position (r=0.914); (3) Compared with multiple single models, the Stacking ensemble model had the highest estimation accuracy (R2=0.807, MAE=0.334, RMSE=0.136). At the same time, its relative prediction deviation (RPD=7.443) was significantly higher than other models, which had excellent prediction ability. This study provides a new idea for obtaining the chlorophyll content of kiwifruit leaves quickly and accurately. |
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