农作物虫害监测需要发展现代检测技术。棉花虫害具有隐蔽性、迁飞性和突发性特点，并且影响因素众多，虫害作用的机理不明确，棉花虫害准确地诊断是该领域的难点问题。虫害诱导挥发物的研究为棉花虫害诊断提供了一种新的思路，已有研究表明电子鼻可以识别虫害诱导的挥发物，获得虫害综合信息，在虫害诊断方面具有很大的应用前景。本项目以受到虫害的花铃期棉花为研究对象，开展基于电子鼻的棉花田间虫害诊断原理和方法研究。具体内容包括：研究电子鼻虫害信息的有效提取方法；结合棉花挥发物GC-MS分析结果，深入探讨电子鼻传感器响应信号与挥发物成分的关联关系；综合考虑各种不确定因素，采用人工神经网络、概率模糊等模式识别方法研究电子鼻及环境因子融合信息与棉花虫害的关联关系，建立棉花虫害诊断模型。本项目旨在通过检测、监测、建模等多种技术手段，揭示电子鼻花铃期棉花虫害诊断的机理，为解决农作物田间虫害诊断难题提供理论和技术支持。

Crop insect pests monitoring requires the development of modern detection technology. Because there are many factors involved, pest damage mechanisms is still not clear. It is difficult to accurately diagnose the cotton pests due to its characteristics of concealment, migration and sudden. Research results on herbivore-induced volatiles provide a new way of thinking for cotton pests diagnosis. According to the present researches, electronic nose(E-nose) can recognize herbivore-induced plant volatiles which contains integrated pests information. Thus, it has a good promotion prospects in pest diagnosis. In this scientific research item, an applied background research on the principles and methods of cotton pests rapid diagnosis during the flower-bolling stage using E-nose. The research content involves: analyzing effective extraction method for pest information using E-nose; exploring the relationship between sensors signal response and volatiles components binding GC-MS; investigating the influence of various uncertain factors and lucubrating the association of electronic nose and environmental factors fusion information with cotton pest information by pattern recognition methods, such as artificial neural network, probabilistic fuzzy etc., and establishing cotton insect pests diagnosis model. This project aims at revealing the E-nose diagnosis mechanism for cotton pests through techniques such as analyzing, monitoring and modeling. The research results achieved in this project could provide effective reference information for crop insect pests diagnosis.