【摘要】 草地贪夜蛾是一种严重破坏农作物的重大洲际害虫,对我国农业生产造成了极大的威胁.尽管一系列防治措施已经展开,但如何有效辨别草地贪夜蛾仍然是防控工作中的一大难题.为了建立一个有效的识别算法,课题组开展了一系列研究工作,主要贡献在于:①采集了不同地域、不同生长区间的草地贪夜蛾及相似物种图片,建立了一个草地贪夜蛾识别数据库;②利用基于特征融合的深度学习算法,建立了一个三通道T型深度卷积神经网络(T-CNN),在现有数据集上平均识别率达到97%,为草地贪夜蛾的智能识别与防控工作提供了技术支撑.更多还原

【Abstract】 Spodoptera frugiperda is a serious crop-destroying pest, which poses a great threat to agricultural production in China. Although a series of preventive measures have been adopted, how to identify the pest effectively is still a major problem in the field. In a study reported in this paper, a series of work was done to establish an effective recognition algorithm. Our main contributions were as follows. First, pictures of S. frugiperda and similar species were collected from different regions, and a recognition database of S. frugiperda was established. Secondly, using a deep-learning algorithm based on feature fusion, we constructed a three-channel T-type deep convolution neural network(T-CNN), whose average recognition rate was over 97% on the existing data sets, thus providing technical support for the smart identification and control of S. frugiperda.更多还原