农机化研究Issue(9)：219-223,5.
基于图像处理新技术的甜菜氮营养无损检测系统的研究
Research on Sugar Beet Nitrogen Nutrition Nondestructive Testing System Based on Image Processing Technology
摘要
Abstract
In this paper, problems common nitrogen fertilizer used in excess of the current agricultural beet production, the establishment of critical real-time accurate nitrogen fertilizer recommendation system.In this paper, by using the BP neural network algorithm using image data to predict nitrogen content of beet, through reasonable excluding abnormal data from the original image data does not match the shooting conditions selected 147 sets of data as a training set, 90 sets of data for the prediction set group.The R, G, B as an input to obtain the predicted value and the actual value of the BP <br> neural network algorithm trained by the best correlation coefficient of r =0 .70 , root mean square error RMSE =4 .60 . The R /(R +G +B), G /(R +G +B), B /(R +G +B) as an input, the use of BP neural network algorithm trained after the predicted value and the actual value of the best correlation coefficient r =0 .64 , root mean square error RMSE =3.66.As can be seen, the use of BP neural network algorithm for establishing beet color feature information ni-trogen model is feasible, provide methodological support for agricultural production in real-time lossless diagnostic beet plant nitrogen content.关键词
甜菜/氮素/预测/BP神经网络/相关系数Key words
beets/nitrogen/forecasting/BP neural network/correlation coefficient分类
农业科技引用本文复制引用
李哲,田海清,王辉,徐琳,李斐,史树德..基于图像处理新技术的甜菜氮营养无损检测系统的研究[J].农机化研究,2016,(9):219-223,5.基金项目
国家自然科学基金项目（41261084）；国家现代农业产业技术体系专项 （）
