大豆豆荚炭疽病严重度的光谱检测

Spectral Detection on Disease Severity of Soybean Pod Anthracnose

利用可见/近红外光谱技术对大豆豆荚炭疽病严重度进行检测。分别采用主成分分析法(PCA)结合反向传输人工神经网络(BPNN)和连续投影算法(SPA)结合BPNN 2种组合模型进行分析预测。利用SPA的数据压缩功能和BPNN的学习预测能力实现对大豆豆荚炭疽病严重度的检测。以样本检测的准确率作为模型评价指标。实验结果显示SPA-BPNN的检测准确率最高,为90%。研究表明,SPA能够有效地进行波长选择,使BPNN模型获得满意的检测率。

Visible and near infrared reflectance （Vis/NIR） spectroscopy technique was applied to detect the disease severity of soybeanpods anthracnose. Principal component analysis （PCA） combined with back propagation neural network （BPNN） and successive projections algorithm （SPA） combined with BPNN were used as two methods to analyze and prediction of the disease severity of soybean pods anthracnose. Data compression of SPA and learning ability of BPNN was used to achieve the detection of anthracnose severity on soybean pods. The accurate rate of identification was used to evaluate the model. The results of experiment showed that SPA - BPNN was the better calibration model and the accurate rate of detection was 90%. According to the results, SPA was a powerful way for the selection of effective wavelengths, and BPNN model could obtain the accurate detection.