Design and Demonstration of Tobacco Pest Monitoring System Based on Visual Recognition Technology

This article aims to develop a tobacco pest monitoring system based on visual recognition technology. In order to achieve this goal, this article uses high-definition image acquisition equipment to obtain real-time images of tobacco growing environment, and combines CNN (Convolutional neural network) technology in DL (Deep learning) to build an algorithm model of pest identification. In the process of model design and implementation, this article analyzes the characteristics of pest images in detail, and designs and optimizes the structure of CNN model. At the same time, a wealth of training data sets have been prepared and carefully marked. The results show that the system can accurately identify a variety of common tobacco pests, and the recognition accuracy is significantly higher than that of manual inspection, and it is excellent in pre-alarm of pests and identification of small pests. The real-time performance of the system has also been verified, which meets the real-time feedback requirements in practical applications. It is concluded that an efficient and accurate tobacco pest monitoring system has been successfully developed in this study, which provides strong support for the intelligent management of tobacco industry.