| 光谱学与光谱分析 |
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| Hyperspectrum Based Prediction Model for Nitrogen Content of Apple Flowers |
| ZHU Xi-cun1, ZHAO Geng-xing1*, WANG Ling1, DONG Fang2, LEI Tong1, ZHAN Bing3 |
1. College of Resources and Environment, Shandong Agricultural University, Tai’an 271018，China
2. College of City Development, Jinan University, Ji’nan 250002，China
3. Bureau of Land and Resource of Qixia City, Qixia 265300，China |
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Abstract The present paper aims to quantitatively retrieve nitrogen content in apple flowers, so as to provide an important basis for apple informationization management. By using ASD FieldSpec 3 field spectrometer, hyperspectral reflectivity of 120 apple flower samples in full-bloom stage was measured and their nitrogen contents were analyzed. Based on the apple flower original spectrum and first derivative spectral characteristics, correlation analysis was carried out between apple flowers original spectrum and first derivative spectrum reflectivity and nitrogen contents, so as to determine the sensitive bands. Based on characteristic spectral parameters, prediction models were built, optimized and tested. The results indicated that the nitrogen content of apple was very significantly negatively correlated with the original spectral reflectance in the 374-696, 1 340-1 890 and 2 052-2 433 nm, while in 736-913 nm they were very significantly positively correlated; the first derivative spectrum in 637-675 nm was very significantly negatively correlated, and in 676-746 nm was very significantly positively correlated. All the six spectral parameters established were significantly correlated with the nitrogen content of apple flowers. Through further comparison and selection, the prediction models built with original spectral reflectance of 640 and 676 nm were determined as the best for nitrogen content prediction of apple flowers. The test results showed that the coefficients of determination (R2) of the two models were 0.825 8 and 0.893 6, the total root mean square errors (RMSE) were 0.732 and 0.638 6, and the slopes were 0.836 1 and 1.019 2 respectively. Therefore the models produced desired results for nitrogen content prediction of apple flowers with average prediction accuracy of 92.9% and 94.0%. This study will provide theoretical basis and technical support for rapid apple flower nitrogen content prediction and nutrition diagnosis.
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Received: 2009-02-16
Accepted: 2009-05-20
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Corresponding Authors:
ZHAO Geng-xing
E-mail: zhaogx@sdau.edu.cn
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