Luo Ju-hua, Huang Wen-jiang, Gu Xiao-he, Ji Ning, Ma Li, Song Xiao-yu, Li Wei-guo, Wei Zhao-ling
National Engineering Research Center For Information Technology in Agriculture, Beijing 100097, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2010 Jan;30(1):184-7.
Forty six points representing different severity degree of stripe rust were established in winter wheat field. The canopy reflectance was collected by an ASD hand-held spectrometer at each point. Meanwhile, the diseases index was investigated. These data were used for the following analysis. Firstly, the relationships between diseases index and reflectance of bands in the range of 300-1500 nm were analyzed. The sensitive bands were selected for stripe rust detecting. Secondly, considering the character of PHI image, red bands (620-718 nm) and near infrared bands (770-805 nm) were assigned as the best bands. Finally, the mean reflectance of red bands (620-718 nm) and near infrared bands (770-805 nm) was calculated respectively to construct the reverse model with the observed diseases indexes: DI = 19.241 R1 - 2.20667 R2 + 12.2744. With this model, the severity degree of stripe rust of winter wheat was monitored successfully in PHI image.
在冬小麦田中设置了46个代表不同条锈病严重程度的点位。在每个点位使用ASD手持式光谱仪采集冠层反射率。同时,调查病害指数。这些数据用于后续分析。首先,分析300 - 1500nm波段范围内病害指数与反射率之间的关系,选择敏感波段用于条锈病检测。其次,考虑PHI图像的特征,将红波段(620 - 718nm)和近红外波段(770 - 805nm)指定为最佳波段。最后,分别计算红波段(620 - 718nm)和近红外波段(770 - 805nm)的平均反射率,构建与观测病害指数的反演模型:DI = 19.241R1 - 2.20667R2 + 12.2744。利用该模型,在PHI图像中成功监测了冬小麦条锈病的严重程度。