为了探索运用数码照片中光谱(红、 绿、 蓝)的像素计算得到的冠层覆盖度(canopy cover, CC)对玉米长势及氮素营养状态进行非破坏性监测的技术。 通过获取玉米冠层的数码照片图像, 定量化数码照片色彩参数与作物叶面积指数(leaf area index, LAI)、 冠层干重(shoot dry matter weight, DM)、 叶片氮素含量(leaf nitrogen content percentage, N%)之间的关系。 试验于2012年和2013年在中国农业科学院试验田进行, 运用基于Visual Basic Version 6.0研发的玉米冠层图像分析系统, 分析了玉米品种中单909在3个氮素水平条件下分别于9叶展时期、 抽雄期和灌浆期的CC、 11种色彩指数与植株LAI, DM, N%及产量之间的相关性, 并对相关性显著的指标进行了拟合与建模。 结果表明, CC与LAI(r=0.93, p＜0.01), DM(r=0.94, p＜0.01), N%(r=0.82, p＜0.01)之间均达到了极显著水平; 用CC估算LAI, DM和N%的模型均为幂函数, 方程式分别是y=3.281 2x0.763 9, y=283.658 1x0.553 6, y=3.064 5x0.932 9; 用与建模相独立的数据对模型验证, 结果表明, CC估算LAI模型的实测值与模拟值基于1∶1直线的R2, RMSE和RE分别是0.996, 0.035和1.46%; CC估算DM模型的R2, RMSE和RE分别是0.978, 5.408 g和2.43%; CC估算N%模型的R2, RMSE和RE分别是0.990, 0.054和2.62%。 综上所述, 模型能够较准确的通过CC估算不同氮肥水平条件下玉米9叶展时期、 抽雄期和灌浆期的LAI, DM与N%, 表明应用数码相机的光谱信息可实现对玉米生长过程中的生长状况及氮素营养状态进行实时无损快速监测与预测。

In order to explore a non-destructive monitoring technique, the use of digital photo pixels canopy cover (CC) diagnosis and prediction on maize growth and its nitrogen nutrition status. This study through maize canopy digital photo images on relationship between color index in the photo and the leaf area index (LAI), shoot dry matter weight (DM), leaf nitrogen content percentage (N%). The test conducted in the Chinese Academy of Agricultural Science from 2012 to 2013, based on Maize canopy Visual Image Analysis System developed by Visual Basic Version 6. 0, analyzed the correlation of CC, color indices, LAI, DM, N% on maize varieties (Zhongdan909, ZD 909) under three nitrogen levels treatments, furthermore the indicators significantly correlated were fitted with modeling, The results showed that CC had a highly significant correlation with LAI (r=0.93, p<0.01), DM (r=0.94, p<0.01), N% (r=0.82, p<0.01). Estimating the model of LAI, DM and N% by CC were all power function, and the equation respectively were y=3.281 2x0.763 9, y=283.658 1x0.553 6 and y=3.064 5x0.932 9; using independent data from modeling for model validation indicated that R2, RMSE and RE based on 1∶1 line relationship between measured values and simulated values in the model of CC estimating LAI were 0.996, 0.035 and 1.46%; R2, RMSE and RE in the model of CC estimating DM were 0.978, 5.408 g and 2.43%; R2, RMSE and RE in the model of CC estimating N% were 0.990, 0.054 and 2.62%. In summary, the model can comparatively accurately estimate the LAI, DM and N% by CC under different nitrogen levels at maize grain filling stage, indicating that it is feasible to apply digital camera on real-time undamaged rapid monitoring and prediction for maize growth conditions and its nitrogen nutrition status. This research finding is to be verified in the field experiment, and further analyze the applicability throughout the growing period in other maize varieties and different planting density.