摘要
利用可见/近红外光谱技术对感染灰霉病的番茄叶片感染程度进行了检测。提出了主成分分析结合BP神经网络的数据处理方法。采用主成分分析进行数据的降维,减少了计算量,提高了建模精度。通过主成分分析中的载荷值,定性地分析了不同波段对病害程度检测的重要性。将得到的最主要的几个主成分输入BP神经网络进行建模,预测结果显示,当主成分数为8,隐含层结点数为11的时候,病害程度的检测模型对未知样本预测的相关系数达到0.930,SEP为0.0687,模型具有良好的检测效果。说明基于光谱技术和化学计量学方法的灰霉病检测模型具有很好的检测能力,为光谱技术应用于病害检测提供了新的方法。
Visible and near-infrared reflectance spectroscopy （Vis/NIRS） technique was applied to the detection of disease level of grey mold on tomato leave. Chemometrics was used to build the relationship between the reflectance spectra and disease level. In order to decrease the amount of calculation and improve the accuracy of the model, principal component analysis （PCA） was executed to reduce numerous wavebands into several principal components （PCs） as input variables of BP neural network. The loading value of PC1 was applied to qualitatively analyze which wavebands were more important for disease detection. Prediction results showed that when the number of primary PCs was 8 and the hidden nodes of BP neural network were 11, the detection performance of the model was good as correlation coefficient （r） was 0. 930 while standard error of prediction （SEP） was 0. 068 7. Thus, it is concluded that spectroscopy technology is an available technique for the detection of disease level of grey mold on tomato leave based on chemometrics used for data analysis.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2007年第11期2208-2211,共4页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(60605011)
浙江省重大科技攻关项目(2005C12029)
宁波市自然科学基金项目(2007A10080)资助
关键词
可见/近红外光谱
灰霉病
番茄
主成分分析
BP神经网络
Near infrared spectroscopy
Grey mold
Tomato
Principal component analysis（PCA）
BP neural network（BPNNS）
