针对单籽粒小麦内卵和低龄幼虫的检测以及内部害虫侵染的准确判别，以小麦中危害最为严重的玉米象、谷蠹侵染的粮粒为研究对象，研究害虫由卵逐步变态发育过程中，粮粒内蛋白质、水分等主要成分含量与密度、显微CT图像特征之间的关系，分析完善粒和含虫粮粒在显微CT图像上的表征强度与分布特性，探索侵染粒内主要化学成分含量变化对显微CT成像的作用机理；研究粮粒成像的位置效应，优化分析粮粒清晰投影图像获取的最佳参数组合，构建基于滤波反投影算法的粮粒三维锥束重建模型；建立粮粒立体结构模型，分析早期侵染对粮粒的微观效应，研究不同虫态的虫蚀规律，探索害虫侵染后显微图像特征的变化规律，确定其三维图像特征的表达；针对粮粒的三维灰度图像和二值图像，提取粮粒的基本统计特征、直方图变换特征、形态特征等；构建适于早期侵染粒和完善粒分类的优化特征空间，建立粮粒智能分类模型，以解决制约单籽粒小麦内卵和低龄幼虫不能准确无损检测的难题。

For the detection of young larvae and eggs inside single grain cereals and the accurate criterion of the internal pest infection, wheat kernels infested by Sitophilus zeamais (Motschulsky) and Rhyzopertha dominica (Fabricius) are used as the research object. The two species of insects do most harm to wheat seriously. During the period from eggs metamorphosis into adult gradually, and the relationship among the main component content such as protein and water，density of wheat kernels and the image features of micro-computed tomography are studied. The characterization intensity and the distribution characteristics on the micro-computed tomography images are analyzed for the healthy kernels and the infested kernels. The action mechanism of changes of main chemical composition content inside the infested kernels to micro-computed tomography imaging will be explored. Studying the position effect of wheat kernels imaging, and obtaining wheat kernels clear projection images of the optimal parameter combination, then it will be optimized and analyzed. The three-dimensional cone beam reconstruction model of wheat kernels will be constructed based on the filtered back projection algorithm. The microscopic effect of early infection on wheat kernels is analyzed based on three-dimensional visual structural model. The insect damage law of the different insect instars is studied. The variation law of the micro-computed tomography image features will be explored to the infested wheat kernels, and then the expression of the three-dimensional image feature are determined. For the three-dimensional gray scale images and binary images of grain kernels, the basic statistical features, histogram transform features and morphological features of wheat kernels image are extracted. The optimal image feature space will be set up for the classification of infested kernels and healthy kernels. The intelligent classification model is established for wheat kernels. The difficult problem that the young larvae and eggs inside single wheat kernels could not be detected non-destructively and accurately will be overcome.