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

Nitrogen (N) deficiency can directly reflect the degree of crop N nutrient deficiency, and it is important to obtain the information of rice N deficiency quickly and in a large area to achieve accurate fertilization of rice. Most of the existing studies focused on the use of UAV remote sensing to monitor rice N nutrition, and less research was conducted on N deficiency itself. Based on the canopy spectral data obtained by UAV hyperspectral remote sensing and rice agronomic data obtained by field sampling, the method of constructing the critical nitrogen concentration curve of northeastern rice was studied, and the nitrogen deficit of rice on this basis was determined; the spectrum in the state of nitrogen deficit approximately equal to 0 was used as the standard spectrum, and ratio, difference and normalized difference transformations on the spectral reflectance data were carried out respectively, and then the competitive adaptive re-weighting sampling method was used to the inversion models of rice nitrogen deficit based on the multivariable linear regression (MLR), extreme learning machine（ELM）and the bat algorithm optimized extreme learning machine（BA-ELM） were constructed by taking the extracted feature bands as input variables and the nitrogen deficit as output variables. The results showed that the equation coefficients a and b of the critical nitrogen concentration curve of northeastern rice were 2.026 and -0.4603, respectively, based on field data, which were consistent with previous studies; compared with other transformation methods, the normalized difference transformation and feature band extraction of the rice canopy spectrum significantly improved the correlation between the canopy spectral reflectance and rice nitrogen deficit, and also improved the inversion of the subsequent inversion model. The BA-ELM inversion model with normalized difference spectra as input predicted significantly better than the rest of the models, with the validation set R2 of 0.8306,，RMSE of 0.8141kg/hm2, which had better estimation of N deficit.