利用蚕豆叶片可见-近红外反射光谱结合导数光谱对健康、 少量、 大量虫害三种等级的实验样本进行光谱特征分析, 并选择虫害检测最优波段。 采用Hadoop, Spark和VMWare虚拟机搭建云计算平台, 使用MLlib机器学习库实现人工神经网络（ANN）和支持向量机（SVM）分类算法, 并对三种等级蚕豆叶片全波段和最优波段光谱进行分类建模与预测。 结果表明ANN虫害光谱分类模型准确率优于SVM虫害光谱分类模型, 并且在云平台上运行效率更高, 同时全光谱波段的预测准确性高于最优波段。 通过扩展光谱数据集, 云计算技术在光谱数据挖掘中的计算效率有显著提升。 云计算分类检测可以为作物生物胁迫光谱识别提供新的技术和方法。

Based on the visible-near infrared reflectance spectra of broad bean leaves, by combining the derivative spectra, we analyzed the spectral characteristics of experiment samples with three levels of pests: healthy leaf, leaf with a small amount of pests and leaf with many pests, and selected the optimum waveband for pest detection. The Hadoop, Spark and VMWare virtual machines were used to build the cloud computing platform, and the MLlib machine learning library was used to realize the classification models of artificial neural network (ANN) and support vector machine (SVM). We also conducted classification modeling and prediction of the full waveband and optimum waveband spectra of broad bean leaves with three levels of pests. The experiment results showed that the ANN pest spectrum classification model had higher accuracy than the SVM pest spectrum classification model, and the ANN model also had higher operating efficiency on the cloud platform.In the meantime, the prediction accuracy for full-waveband spectrum was higher than that for optimum waveband. By expanding the spectrum datasets, the computational efficiency of clouding computing technology in spectrum data mining can be significantly improved. The classification detection based on cloud computing can provide new technique and method for the spectral recognition of crop biotic stress.