摘要
棉花是我国重要的经济作物,其在生长过程中极易受虫害影响。因此,准确识别棉花害虫并确定其危害程度,为化学防治提供依据意义重大。文章设计了一种基于Faster-RCNN模型的棉花虫害识别方法,并统计其类型和数量参数。试验结果表明:与CNN+RCNN模型相比,单RCNN模型耗时长,且准确率低。该研究工作可为智能化虫害识别系统的开发提供一定的方法基础。
Cotton is an important economic crop in China,it is highly susceptible to insect pests during its growth.Therefore,accurately identifying cotton pests and determining their degree of harm is of great significance for providing a basis for chemical control.In this paper,we propose a cotton pest identification method based on the Faster RCNN model and count its type and quantity parameters.Experimental results show that compared with the CNN+RCNN model,the single RCNN model has a longer processing time and lower accuracy.This study provides a certain methodological foundation for the development of intelligent pest identification systems.
出处
《大众科技》
2023年第5期5-7,12,共4页
Popular Science & Technology
基金
永州市指导性科技计划项目(2021-YZKJZD-007)。
