基于调制荧光检测技术的柑橘黄龙病诊断

Citrus Huanglongbing detection based on modulation chlorophyll fluorescence measurement

摘要:
目的实现柑橘黄龙病的及时诊断，防止病情扩散、保障柑橘生产。
方法运用基于调制荧光检测技术的超便携式调制叶绿素荧光仪MINI-PAM获取柑橘Citrus reticulata叶片荧光参数，通过概率神经网络对荧光数据进行建模及分类处理，以鉴定并区分健康的、非黄龙病黄化的以及黄龙病的柑橘植株。
结果该方法对所有类别的诊断准确率均高于76.93%，有些类别分类准确率甚至可达100%。
结论基于概率神经网络的柑橘黄龙病调制荧光检测技术用于鉴别柑橘黄龙病病情具有一定的可行性和推广性。

Abstract:
Objective To diagnose citrus Huanglongbing(HLB) timely to prevent citrus production from the spread of the disease.
Method A detection method of citrus HLB based on modulation chlorophyll fluorescence measurements was investigated. Fluorescence parameters were extracted from MINI-PAM, and analyzed by probability neural network (PNN) model and classification to distinguish among healthy citrus, HLB-infected citrus and etiolated citrus due to non-HLB problems.
Result The average detection accuracy for different classes of citrus symptoms was above 76.93%, and that for some classes even reached 100%.
Conclusion It is feasible to use the modulation chlorophyll fluorescence measurement combined with PNN model to detect citrus HLB.