التفاصيل البيبلوغرافية
| العنوان: |
HYPERSPECTRAL ESTIMATION OF WHEAT CHLOROPHYLL CONTENT BASED ON PRINCIPAL COMPONENT ANALYSIS. |
| المؤلفون: |
MA, C. Y., SHI, J.-J., WU, X.-F., GUO, M., LI, C.-C. |
| المصدر: |
Applied Ecology & Environmental Research; 2023, Vol. 21 Issue 6, p5009-5037, 29p |
| مصطلحات موضوعية: |
PRINCIPAL components analysis, CHLOROPHYLL, WHEAT, SUPPORT vector machines, REMOTE sensing, NUTRITIONAL status |
| مستخلص: |
Chlorophyll content is an important index to measure the nutritional status of wheat. Rapid and accurate estimation of chlorophyll content is crucial to monitor the photosynthetic capacity and growth status of wheat and optimize its quality. To solve the problem of low precision in the hyperspectral estimation of crop chlorophyll content, this paper selects vegetation indices, spectral characteristic parameters, fractional differential spectrum, and wavelet energy coefficient as index parameters. Meanwhile, principal component analysis (PCA) is exploited to reduce and fuse these index parameters to eliminate the multicollinearity among the index parameters. Then, based on multiple linear regression and support vector machine algorithms, the estimation model of wheat chlorophyll content in different growth stages is constructed. The results show that the PCA reduces the dimension of hyperspectral data while retaining the original information, which improves the operation efficiency of the model, and ensures the effect of chlorophyll content estimation. The experimental results indicate that the multiple linear regression method achieves a better estimation effect of chlorophyll content at the booting stage, and the R2, RMSE, and nRMSE of the estimation model are 0.79, 2.21, and 5.50% respectively. This study provides a new technical method for estimating crop chlorophyll content using hyperspectral remote sensing data and comprehensive index parameters. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
