CAJ | 학술논문

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（R 2）更大。
건립기우동소맥관층도상분석획취적관층복개도화색채지수적지상부생물량고산모형，이촉진작물관층도상분석기술화 BP 신경망락기술재동소맥장세무손감측중적응용。륙개시담수평적전간시험조건하，재동소맥발절기，분사차채집동소맥관층도상，동보진행파배성취양，측정동소맥지상부생물량；분석료통과도상분석연건（이용미연 Visual Basic 연건개발）획취적관층복개도화10충색채지수여동소맥지상부생물량적상관관계，이축보회귀화 BP 신경망락방법건립료동소맥지상부생물량고산모형。결과표명，동소맥지상부생물량여관층복개도、포화도화홍광량도치정현저상관，기중，여관층복개도간적상관성최강，차제량도외，관층복개도、색채지수여지상부생물량간정비선성상관。통과 BP 신경망락방법구건적모형상대우축보회귀모형，현저제고료동소맥지상부생물량고산정도，균방근오차（RMSE）、상대균방근오차（RRMSE）경소，결정계수（R 2）경대。
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.