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

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    Digital image analysis and machine learning accurately estimate winter wheat leaf nitrogen accumulation (LNA) at the canopy level. Artificial neural network and support vector regression models demonstrated strong predictive performance and generalization ability.

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    Area of Science:

    • Agricultural Science
    • Plant Physiology
    • Digital Image Analysis

    Background:

    • Accurate estimation of leaf nitrogen accumulation (LNA) is crucial for optimizing winter wheat fertilization and yield.
    • Traditional LNA measurement methods are labor-intensive and destructive.
    • Digital image analysis offers a non-invasive approach for crop monitoring.

    Purpose of the Study:

    • To assess the feasibility of using digital image analysis and machine learning algorithms for estimating winter wheat LNA at the canopy level.
    • To identify key image-derived parameters correlated with LNA.
    • To compare the performance of different regression and machine learning models for LNA estimation.

    Main Methods:

    • Digital images of winter wheat canopies were captured during the elongation stage under varying nitrogen levels.
    • Random forest algorithm was employed for image segmentation and extraction of canopy cover (CC), RGB color components, and five color indices.
    • Correlation analysis was performed between LNA and extracted image parameters.
    • Nonlinear least squares (NLS), artificial neural network (ANN), support vector regression (SVR), and random forests (RF) models were developed to estimate LNA.

    Main Results:

    • Canopy cover (CC), R and G components of sRGB color space, and five derived color indices showed significant correlations with LNA.
    • CC exhibited the highest correlation with LNA.
    • NLS and RF models demonstrated lower accuracy, with RF showing overfitting.
    • ANN and SVR models, using CC and RGB components, achieved high R2 values (0.851 and 0.862, respectively) and low RMSE (19.440 and 18.698, respectively).

    Conclusions:

    • Digital image analysis combined with machine learning, particularly ANN and SVR, provides a reliable and accurate method for estimating winter wheat LNA non-destructively.
    • Canopy cover and RGB color components are effective predictors of LNA.
    • These findings support the application of advanced imaging techniques for precision agriculture and crop management.