中文摘要：

为了解决果园环境中单一的害虫监测技术存在的缺陷,该研究将红外传感器和机器视觉识别技术进行融合,从两个角度对目标害虫进行识别计数。选取梨小食心虫、苹小卷叶蛾、桃蛀螟、干扰物进行试验,通过实验室人工随机散落试验样本,获得其红外传感器以及机器视觉图像的识别结果,构造融合计数计算公式,通过计算得到害虫计数结果。结果显示：梨小食心虫、苹小卷叶蛾、桃蛀螟、干扰物的红外分类阈值分别为2.25、9.06、17.88、28.38,其红外识别范围分别为（0,5]、（5,13]、（13,23]、（23,32];梨小食心虫、苹小卷叶蛾、桃蛀螟、干扰物的红外识别准确率分别为92%、78%、80%、88%,图像识别准确率分别为92%、88%、92%、90%,融合计数精度分别为98%、92%、94%、96%。可见,将红外传感器和图像识别技术相融合能够提高果树性诱害虫的识别准确率,为果园害虫的合理防治提供参考。

英文摘要：

Traditional single monitoring technique in orcha rd environment has such shortages as weak effectiveness, inaccurate count and pooruniversality. Now existing pest monitoring methods include acoustic measurement, piezoelectric measurement, infrared measurement and machine vision recognition technology. In view of this, the future development trend of pest detection technology will undoubtedly be a variety of detection methods combined with each other. Comprehensive utilization of the existing testing methods will form a multiple information fusion technique to detect and provide reliable scientific decision based on comprehensive prevention and control of fruit pests, and the loss will be reduced to a minimum. In this paper, infrared measurement and machine vision recognition technology are integrated to identify pest species and count pest populations, and information of pests is obtained from 2 aspects. The accuracy of the fusion result is verified by comparing with the manual count. Taking Grapholitha molesta, Dichocrocis punctiferalis, Adoxophyes orana and disruptors as research objects, recognition results of infrared sensors and machine vision are obtained using the laboratory artificially randomly scattered test samples. Test samples were collected in Xiaotangshan National Precision Agriculture Research and Demonstration Base from July to September in 2015. For the infrared method, infrared circuit is mainly composed of infrared detector, photoelectric detector, filter, amplifier, communication module, and so on. Due to the different size of insect pests, the infrared output is different. The bigger the pest, the bigger the value of the infrared output. Therefore, the influence of ambient light on the detection results is significant. For example, Adoxophyes orana is larger than Grapholitha molesta and smaller than Dichocrocis punctiferalis. To go along with this, the thresholds of Grapholitha molesta, Adoxophyes orana, Dichocrocis punctiferalis and disruptors are 5.655, 13.47 and 23.13, respectively. The s