数字农科院2.0

Estimation of Winter Wheat Leaf Nitrogen Accumulation using Machine Learning Algorithm and Visible Spectral

文献类型： 外文期刊

作者： Cui Ri-xian;Liu Ya-dong;Fu Jin-dong

作者机构：

关键词： Winter wheat;Machine learning algorithm;Visible spectrum;Color indices;Canopy cover;Leaf nitrogen accumulation

期刊名称： SPECTROSCOPY AND SPECTRAL ANALYSIS

ISSN： 1000-0593

年卷期： 2016 年 36 卷 6 期

页码：

收录情况： SCI

摘要： In order to study the feasibility of using digital image analysis and machine learning algorithm to estimate leaf nitrogen accumulation (LNA) of winter wheat at canopy level, digital images of winter wheat canopies grown under six levels of nitrogen application rate were taken for four times during the elongation stage. Meanwhile, wheat plants were sampled to measure LNA. The random forest method using CIEL *a*b* components was used to segment wheat plant from soil background and then extract canopy cover, RGB components of sRGB color space and compute five color indices derived from RGB components. Correlation analysis was carried out to identify the relationship between LNA and canopy cover (CC), RGB components, and five color indices. Two kinds of nonlinear least squares regression models (NLS) with different independent variables of color components and color indices, and three machine learning algorithmic of artificial neural network (ANN), support vector regression (SVR), and random forests method (RF) were used to estimate winter wheat leaf nitrogen accumulation. All three machine learning algorithm had four input variables of CC, R, G, and B. The results showed that, CC, R and G component of sRGB color space, and five color indices derived from RGB components showed significant correlations with LNA during the elongation stage. CC revealed the highest correlation with LNA. The lowest accuracy in estimation LNA was achieved by using nonlinear least square model with CC and color indices, and RF had showed the problem of overfitting. The other three methods of LNA with CC and RGB components, ANN, and SVR had showed good performance with higher R-2 (0. 851, 0. 845, and 0. 862) and lower RMSE (19. 440, 19. 820, and 18. 698) for model calibration and validation, revealing good generalization ability.

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