基于可见光光谱和BP人工神经网络的冬小麦生物量估算研究

Estimation of Winter Wheat Biomass Using Visible Spectral and BP Based Artificial Neural Networks

建立基于冬小麦冠层图像分析获取的冠层覆盖度和色彩指数的地上部生物量估算模型,以促进作物冠层图像分析技术和BP神经网络技术在冬小麦长势无损监测中的应用。六个施氮水平的田间试验条件下,在冬小麦拔节期,分四次采集冬小麦冠层图像,同步进行破坏性取样,测定冬小麦地上部生物量;分析了通过图像分析软件(利用微软Visual Basic软件开发)获取的冠层覆盖度和10种色彩指数与冬小麦地上部生物量的相关关系,以逐步回归和BP神经网络方法建立了冬小麦地上部生物量估算模型。结果表明,冬小麦地上部生物量与冠层覆盖度、饱和度和红光亮度值呈显著相关,其中,与冠层覆盖度间的相关性最强,且除亮度外,冠层覆盖度、色彩指数与地上部生物量间呈非线性相关。通过BP神经网络方法构建的模型相对于逐步回归模型,显著提高了冬小麦地上部生物量估算精度,均方根误差(RMSE)、相对均方根误差(RRMSE)更小,决定系数(R2)更大。

The objective of this study was to evaluate the feasibility of using color digital image analysis and back propagation （BP）based artificial neural networks （ANN）method to estimate above ground biomass at the canopy level of winter wheat field. Digital color images of winter wheat canopies grown under six levels of nitrogen treatments were taken with a digital camera for four times during the elongation stage and at the same time wheat plants were sampled to measure above ground biomass.Cano-py cover （CC）and 10 color indices were calculated from winter wheat canopy images by using image analysis program （developed in Microsoft Visual Basic）.Correlation analysis was carried out to identify the relationship between CC,10 color indices and winter wheat above ground biomass.Stepwise multiple linear regression and BP based ANN methods were used to establish the models to estimate winter wheat above ground biomass.The results showed that CC,and two color indices had a significant cor-relation with above ground biomass.CC revealed the highest correlation with winter wheat above ground biomass.Stepwise mul-tiple linear regression model constituting CC and color indices of NDI and b,and BP based ANN model with four variables （CC, g,b and NDI）for input was constructed to estimate winter wheat above ground biomass.The validation results indicate that the model using BP based ANN method has a better performance with higher R 2 （0.903）and lower RMSE （61.706）and RRMSE （18.876）in comparation with the stepwise regression model.