基于深度学习的烟叶虫害智能检测系统设计

Design of intelligent detection system for tobacco leaf pests based on deep learning

随着农业科技的进步,传统的虫害监测方法已经无法满足现代农业的需求。为了提高烟叶生产效率和质量,文章设计并实现了一种基于深度学习的烟叶虫害智能检测系统。该系统采用了先进的YOLOv7目标检测模型,同时集成了数据采集、深度学习训练、虫害检测等功能,能够快速准确地识别烟叶上的虫害,有助于提前介入查杀,保护烟叶质量。测试结果表明,该系统在实时监测和准确性方面表现优异,提升了虫害管理的效率。

With the advancement of agricultural science and technology,the traditional pest monitoring methods can no longer meet the needs of modern agriculture.In order to improve the efficiency and quality of tobacco leaf production,this paper designs and implements an intelligent detection system for tobacco leaf pests based on deep learning.The system uses the advanced YOLOv7 target detection model and integrates functions such as data collection,deep learning training,and pest detection,can quickly and accurately identify insect pests on tobacco leaves,helping to intervene in early killing and protect the quality of tobacco leaves.The test results show that the system performs well in terms of real-time monitoring and accuracy,improving the efficiency of pest management.