Improving estimation accuracy of canopy nitrogen content using the fusion of ground-space spectral imagery with machine learning in pear orchards

Abstract

Accurate and rapid monitoring canopy-scale nitrogen content (CNC) on pear trees is crucial for precise application of nitrogen fertilizer. Unmanned Aerial Vehicle (UAV)-based spectral analysis is becoming a promising solution for fast monitoring plant nutrition. However, complex data collection conditions, e.g., unpredictable local microclimate, in orchards could easily compromise the quality of spectral images, thereby affecting the estimation accuracy of CNC inversion model. This study aimed to enhance the quality of canopy-scale raw spectral images to improve the accuracy of CNC inversion through the fusion of ground-space spectral imagery. Firstly, collected leaf-scale hyperspectral images, i.e., spectral reflectance and color data, were used as reference values to enhance the quality of canopy-scale raw multispectral images through constructing mapping models based on machine learning algorithms. The conversion of spectral reflectance and color data between leaf-scale and canopy-scale were conducted using the 4SAIL model and the CIELAB color space, respectively. Then, according to the accuracy of mapping models from four classic machine learning algorithms, the RF algorithm was the optimal choice for constructing CNC inversion models. Furthermore, 10 Vegetation Indexes (VIs), 6 Color Indexes (CIs), and their combinations were analyzed using fitting models with simulated canopy-scale spectral reflectance and leaf-scale color data. Based on the top three R² and RMSE values in each type of model, CNC inversion models were constructed using single VI, single CI, and combinations of VIs and CIs. Meanwhile, four methods were tested in each inversion model. The experimental results showed that the R² and RMSE values of the models using mapped data were averagely improved 0.066 and 0.006, respectively, compared to those using raw canopy reflectance and color data. Among all the inversion models using the mapped data, the combination 1 (C1) inversion model (7 VIs and 2 CIs) performed the best, with R², RMSE, nRMSE, and MAE values reaching 0.832, 0.155, 8.333%, and 0.152, respectively. Finally, compared to the C1 inversion model, by screening the inversion results from multi model, the R² of CNC inversion model increased 0.089, enhancing to 0.921. Meanwhile, the RMSE, nRMSE, and MAE decreased 0.038, 2.043%, and 0.072. reaching 0.117, 6.290%, and 0.080, respectively. This study effectively improved the accuracy of CNC inversion by the fusion of ground-space spectral imagery and can offer reference for the application of nitrogen fertilizer in pear orchards.

Plant phenotyping relevance

ナシ樹冠の窒素含量という植物形質を、地上・空撮スペクトル画像の融合と機械学習で推定する手法の開発・精度検証が研究の中心である。

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