关键词: 无人机, 光谱特征, 纹理特征, 机器学习, 植株氮含量, 迁移能力

Abstract: 【Objective】Accurate monitoring and rational application of nitrogen are particularly important for healthy growth, yield and quality improvement of wheat, and reduction of environmental pollution and resource waste. To develop UAV-based models for accurately and effectively assessment of the plant nitrogen content in the key growth stages of wheat growth, explore the transferability of the models constructed based on machine learning methods. 【Method】Winter wheat experiment plots in Shang shui country, Henan province, China were conducted from 2020 to 2022. Based on the K6 multichannel imager mounted on DJM600 UAV, 5-band (Red, Green, Blue, Red edge, and Nir) multispectral images were obtained from a UAV system in the stages of jointing, booting, flowering and filling in winter wheat, and used to calculate 20 vegetation indices and 40 texture features from different band combinations. Correlation analysis was used to screen the sensitive characteristics of nitrogen content in winter wheat plants from the 65 image features. 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 regression methods were used to build plant nitrogen content models, and compared for the model performance and transferability. 【Result】(1)The correlation coefficients between plant nitrogen content and image features passed the test of 0.01 extremely significant level, including 22 spectral features and 29 texture features. (2) 51 spectral and texture features were adopted 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 from the BP and SVR methods were relatively scattered. The RF method was the best, with R², RMSE, and MAE of 0.81, 0.42%, and 0.30%, respectively; The SVR method was the worst, with R², RMSE, and MAE of 0.66, 0.54% and 0.40 %, respectively. (3) The prediction effects of the four methods on the nitrogen content of W0 and W1 treatments trained using W1 and W0 treatments were the same as those trained using both W0 and W1 datasets, both of which were closer to the 1:1 line for the RF and Adaboost methods. The R² of transfer prediction results for the models constructed by BP, RF, Adaboost, and SVR methods were 0.75, 0.72, 0.72, and 0.66 for the prediction of nitrogen content in W0 treatment and 0.51, 0.69, 0.61 (trained using data from W1 treatment) and 0.45 for the prediction in W1 treatment (trained using data from W0 treatment), respectively.【Conclusion】All models showed strong transferability, especially the RF and Adaboost methods, in predicting winter wheat nitrogen content under rainfed and irrigation water management.

Key words: UAV, spectral feature, textural feature, machine learning, nitrogen content in winter wheat, transferability