ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

In order to provide a rapid, non-destructive, and accurate prediction method for nitrogen content in rice using spectral data, focusing on northeast rice as the research object, hyperspectral data of rice in three growth stages were collected, and combined with indoor chemical experiments, aiming to improve the prediction accuracy and model interpretability of nitrogen content by establishing an inversion model for rice nitrogen content. The acquired hyperspectral data and corresponding nitrogen content of rice leaves were firstly preprocessed by using a low-pass filtering method. For the processed spectral data, a coupling discrete wavelet transform and first-order differential transform (DWT-DE transform) were used for dimensionality reduction, and compared with principal component analysis (PCA) and discrete wavelet multiresolution decomposition methods. The dimensionality-reduced results were used as inputs, and the measured leaf nitrogen content was the output, to establish inversion models by using extreme learning machine (ELM), particle swarm optimization support vector machine (PSO-SVM), and artificial hummingbird algorithm optimized extreme learning machine (AHA-ELM), respectively, for predicting and validating rice leaf nitrogen content. The results showed that the AHA-ELM model established using the results of the coupling discrete wavelet and first-order differential transform had the highest prediction accuracy, which was superior to the ELM and PSO-SVM models. The determination coefficient R2 of the training set was 0.8064, and the root mean square error RMSE was 0.3251mg/g. The R2 of the validation set was 0.7915, and the RMSE was 0.3620mg/g. Therefore, the proposed AHA-ELM model established by DWT-DE transform had significant advantages in the rapid detection of rice nitrogen content, and can provide a good reference for precise variable fertilization in rice.