Comparison and Transferability of Nitrogen Content Prediction Model Based in Winter Wheat from UAV Multispectral Image Data

Abstract

Information about the nitrogen dynamic in wheat is important for improving in-season crop precision nutrient management and cultivated land sustainability. To develop unmanned aerial vehicle (UAV)-based spectral models for an accurate and effective assessment of the plant nitrogen content in the key stages (jointing, booting, and filling) of wheat growth, winter wheat experiment plots in Henan Province, China, were used in this study. Based on the K6 multichannel imager, 5-band (Red, Green, Blue, Red edge, and Near-infrared (Nir)) multispectral images were obtained from a UAV system and used to calculate 20 vegetation indices and 40 texture features from different band combinations. Combining the sensitive spectral features and texture features of the nitrogen content of winter wheat plants, BP neural network (BP), random forest (RF), Adaboost, and support vector machine (SVR) machine learning methods were used to construct plant nitrogen content models, and compared for the model performance and transferability. The results showed that the characteristics of different spectral features were different, but most of them had a partial normal distribution. Compared with spectral features, the distribution of texture features was more discrete. Based on Pearson’s correlation analysis, 51 spectral and texture features were selected to build four machine learning models. The estimates of plant nitrogen by the RF and Adaboost methods were relatively concentrated, mostly close to the 1:1 line; while the estimates of plant nitrogen from the BP and SVR methods were relatively scattered. The RF method was the best, with coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) of 0.811, 4.163, and 2.947 g/m2, respectively; the SVR method was the worst, with R2, RMSE, and MAE of 0.663, 5.348, and 3.956 g/m2, respectively. All models showed strong transferability, especially the RF and Adaboost methods, in predicting winter wheat nitrogen content under rainfed and irrigation water management.