基于改进YOLOv5s的自然环境下茶叶病害识别方法OA北大核心CSCDCSTPCD
Method for identifying tea diseases in natural environment using improved YOLOv5s
针对现有目标检测模型对自然环境下茶叶病害识别易受复杂背景干扰、早期病斑难以检测等问题,该研究提出了YOLOv5-CBM茶叶病害识别模型.YOLOv5-CBM以YOLOv5s模型为基础,在主干特征提取阶段,将一个带有Transformer的C3模块和一个CA(coordinate attention)注意力机制融入特征提取网络中,实现对病害特征的提取.其次,利用加权双向特征金字塔(BiFPN)作为网络的Neck,通过自适应调节每个尺度特征的权重,使…查看全部>>
Tea is easily affected by diseases in the growing process,leading to the decline of tea yield and quality.The conventional visual judgement on the disease cannot fully meet the large-scale production in recent years,due to the low accuracy,time-consuming and laborious.Therefore,an accurate knowledge of tea diseases is in high demand for timely and effective prevention and control measures,in order to reduce the abuse of pesticides for the stable development …查看全部>>
陈禹;吴雪梅;张珍;闫建伟;张富贵;喻丽华
贵州大学机械工程学院,贵阳 550025贵州大学机械工程学院,贵阳 550025贵州大学机械工程学院,贵阳 550025贵州大学机械工程学院,贵阳 550025贵州大学机械工程学院,贵阳 550025贵州大学机械工程学院,贵阳 550025
农业工程
图像识别茶叶病害注意力机制目标检测Yolov5BiFPN
image recognitionteadiseaseattention mechanismtarget detectionYolov5BiFPN
《农业工程学报》 2023 (24)
185-194,10
国家重点研发计划(课题)项目(2021YFD1100307-3)贵州省农业产业技术体系建设专项经费贵州省科技创新基地建设项目(黔科合中引地[2023]010)
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