温室蔬菜病害的发生及大面积流行严重影响设施农业的生产管理, 大大降低设施农业的经济效益。 为了实现温室蔬菜病害的无损准确预测, 以黄瓜霜霉病害为例, 利用激光诱导叶绿素荧光构建光谱特征指数, 建立了温室蔬菜病害的预测模型。 在试验中采用对比分析的方法, 通过对作物健康叶片接种病菌孢子, 分别采集健康、 接种2　d、 接种6　d和出现明显病症共4组试验样本的光谱曲线, 定性分析了荧光强度随叶片样本感染病菌孢子的变化规律; 利用光谱曲线不同波段峰谷值创建了叶绿素荧光光谱指数k1=F685/F512和k2=F734/F512, 根据数值的变化范围, 设定k1和k2分别为20和10时可以作为判断样本出现明显病症与未出现明显病症的特征值, 其判断的准确率分别达到96%和94%; 利用构建的光谱指数与样本健康状况的分类结果, 选择光谱指数F685/F512, F685-F734, F715/F612可以定性判断样本健康状况, 并选择光谱指数F685/F512, F734/F512, F685-F734, F715/F612作为建立定量分析模型的输入量, 以预测集分类准确率作为评价标准, 对比判别分析、 BP神经网络、 支持向量机三种数据建模方法, 结果表明支持向量机作为霜霉病害预测的建模方法, 其预测能力达到91.38%。 利用激光诱导叶绿素荧光构建光谱指数方法, 研究植物病害的预测问题, 具有很好的分类和鉴别效果。

The occurrence of greenhouse vegetable diseases and its epidemic seriously affect the production and management of facility agriculture, which greatly reduce the economic benefits of facility agriculture. In order to achieve nondestructive and accurate prediction of greenhouse vegetable diseases, this paper taking cucumber downy mildew disease as the research object, constructed spectrum characteristic index by using chlorophyll fluorescence induced by laser and established the prediction model of greenhouse vegetable diseases. In this paper, the experiment used comparative analysis method. The healthy leaves of the crops were inoculated with the pathogen spores, the spectrum curves of four groups of test samples: healthy, 2 d inoculated, 6 d inoculated and the ones with obvious symptoms were collected; then qualitative analysis was given to the variation regulation of the fluorescence intensity with the leaf samples infected with the pathogen spores. The chlorophyll fluorescence spectrum index k1=F685/F512 and k2=F734/F512 were created by using the peak and valley values of different bands. According to the range of values, set k1=20 and k2=10 as the characteristic value to judge the sample with obvious symptoms or with no obvious symptoms, and the accuracy rate of the judgment was 96% and 94% respectively. Based on spectrum index created and the classification results of sample health status, we selected the spectrum index F685/F512, F685－F734, F715/F612 to determine the health status of the sample and selected spectrum index F685/F512, F734/F512, F685－F734, F715/F612 as the inputs of quantitative analysis model. Regarding classification accuracy of prediction set as the evaluation criteria, we compared three data modeling methods: discriminant analysis, BP neural network and support vector machine. The results showed that the forecasting ability can reach 91.38% when the support vector machine was used as the modeling method for predicting the downy mildew disease. Use the method with chlorophyll fluorescence induced by laser to construct spectrum index to study the prediction of plant diseases, which has a good classification and identification effect.