数字农科院2.0

Establishment of The Crop Growth and Nitrogen Nutrition State Model Using Spectral Parameters Canopy Cover

文献类型： 外文期刊

作者： Tao Zhi-qiang;Bagum, Shamim Ara;Ma Wei;Zhou Bao-yuan;Fu Jin-dong;Cui Ri-xian;Sun Xue-fang;Zhao Ming

作者机构：

关键词： Maize;Canopy image;Canopy cover;Leaf area index;Dry matter weight;Nitrogen

期刊名称： SPECTROSCOPY AND SPECTRAL ANALYSIS

ISSN： 1000-0593

年卷期： 2016 年 36 卷 1 期

页码：

收录情况： SCI

摘要： In order to explore a non-destructive monitoring technique, the use of digital photo pixels canopy cover (CC) diagnosis and prediction on maize growth and its nitrogen nutrition status. This study through maize canopy digital photo images on relationship between color index in the photo and the leaf area index (LAI), shoot dry matter weight (DM), leaf nitrogen content percentage (N%). The test conducted in the Chinese Academy of Agricultural Science from 2012 to 2013, based on Maize canopy Visual Image Analysis System developed by Visual Basic Version 6. 0, analyzed the correlation of CC, color indices, LAI, DM, N% on maize varieties (Zhongdan909, ZD 909) under three nitrogen levels treatments, furthermore the indicators significantly correlated were fitted with modeling, The results showed that CC had a highly significant correlation with LAI (r=0. 93, p<0. 01), DM (r=0. 94, p<0. 01), (r=0. 82, p<0. 01). Estimating the model of LAI, DM and N% by CC were all power function, and the equation respectively were y=3. 281 2x(0.763 9), y=283. 658 1x(0.553 6) and y=3. 064 5x(0.932 9); using independent data from modeling for model validation indicated that R-2, RMSE and RE based on 1 : 1 line relationship between measured values and simulated values in the model of CC estimating LAI were 0. 996, 0. 035 and 1. 46%; R-2, RMSE and RE in the model of CC estimating DM were 0. 978, 5.408 g and 2.43%; R-2, RMSE and RE in the model of CC estimating N% were 0. 990, 0. 054 and 2. 62%. In summary, the model can comparatively accurately estimate the LAI, DM and N% by CC under different nitrogen levels at maize grain filling stage, indicating that it is feasible to apply digital camera on real-time undamaged rapid monitoring and prediction for maize growth conditions and its nitrogen nutrition status. This research finding is to be verified in the field experiment, and further analyze the applicability throughout the growing period in other maize varieties and different planting density.

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