中国农业大学学报2018，Vol.23Issue(6)：130-139,10.DOI:10.11841/j.issn.1007-4333.2018.06.15
基于数码图像的甜菜氮素近地遥感监测模型
Remote sensing model for monitoring nitrogen in beet based on digital imaging
摘要
Abstract
To explore the feasibility of monitoring nitrogen elements in beet canopy leaves based on digital imaging, field experiments with different planting schemes were carried out in Chifeng City, Inner Mongolia on 2014. To examine the effects of N status on beet growth and color information, four N treatments ranging from 0 to 163 kg/hm2 were applied for growing season. Digital images of beet grown under different nitrogen application rates were taken for several times during the whole growth stage. Meanwhile, the beet plants were sampled to measure leaf nitrogen content(LNC). Digital image processing technology was used to extract image color information including the values of red, green, and blue light, and 9 color indices such as R/B, G/B were computed. In searching key growth stages and the best color parameters suitable for monitoring nitrogen nutrition of beet, correlation between the above color indices with beet LNC was analyzed, and the change regulation of canopy LNC with nitrogen application rate was studied. LNC prediction model of beet canopy was established using support vector machine(SVM)and back-propagation artificial neural network(BP-ANN)respectively. The results indicated that the BP-ANN model has higher and more stable prediction accuracy. The R2 and RMSE of verification set were 0.74 and 2.35, respectively. Compared with the SVM model, the R2 of BP-ANN increased by 12.12％, and RMSE was reduced by 8.09％ respectively. Above results indicated that digital image processing technique could be used for the non-destructive diagnosis of crop nitrogen nutrition.关键词
甜菜/氮素监测/颜色参数/叶片氮含量/数码图像Key words
beet/nutrition monitoring/color parameter/leaf nitrogen concentration/digital image分类
农业科技引用本文复制引用
张珏,田海清,李哲,李斐,史树德..基于数码图像的甜菜氮素近地遥感监测模型[J].中国农业大学学报,2018,23(6):130-139,10.基金项目
国家自然科学基金项目(41261084) （41261084）
内蒙古自然科学基金项目(2016MS0346) （2016MS0346）
