传统的家禽卫生监测方式主要是靠人工操作,既效率低、主观性强,又很难满足现代集约化养殖的需要。本文阐述以Stacking多模型融合技术为基础的禽类健康检测系统,通过CNN、ResNet、AlexNet、VGG等深度学习模型的融合,使禽类粪便图像分类...传统的家禽卫生监测方式主要是靠人工操作,既效率低、主观性强,又很难满足现代集约化养殖的需要。本文阐述以Stacking多模型融合技术为基础的禽类健康检测系统,通过CNN、ResNet、AlexNet、VGG等深度学习模型的融合,使禽类粪便图像分类的精确性、系统实时性和轻量化性能得到显著提升。实验结果表明,该系统的总体准确度为0.9919,优于单一模型,验证了多模型集成的有效性。文章还从监测结果出发,对家禽养殖业进行资源利用的策略分析,技术上的支持使家禽养殖业的智能化、可持续发展,此外系统还可以向其他农业影像分析场景进行推广,实现应用的广泛性。Traditional poultry hygiene monitoring methods mainly rely on manual operation, which is inefficient, subjective, and difficult to meet modern requirements the need for intensive farming. This article describes a poultry health detection system based on Stacking multi model fusion technology. Through the fusion of deep learning models such as CNN, ResNet, AlexNet, and VGG, the accuracy, real-time performance, and lightweight performance of poultry manure image classification have been significantly improved. The experimental results show that the overall accuracy of the system is 0.9919, which is better than a single model and verifies the effectiveness of multi model integration. The article also analyzes the strategy of resource utilization in the poultry farming industry based on monitoring results. Technical support enables the intelligent and sustainable development of the poultry farming industry. In addition, the system can be promoted to other agricultural image analysis scenarios to achieve widespread application.展开更多
文摘传统的家禽卫生监测方式主要是靠人工操作,既效率低、主观性强,又很难满足现代集约化养殖的需要。本文阐述以Stacking多模型融合技术为基础的禽类健康检测系统,通过CNN、ResNet、AlexNet、VGG等深度学习模型的融合,使禽类粪便图像分类的精确性、系统实时性和轻量化性能得到显著提升。实验结果表明,该系统的总体准确度为0.9919,优于单一模型,验证了多模型集成的有效性。文章还从监测结果出发,对家禽养殖业进行资源利用的策略分析,技术上的支持使家禽养殖业的智能化、可持续发展,此外系统还可以向其他农业影像分析场景进行推广,实现应用的广泛性。Traditional poultry hygiene monitoring methods mainly rely on manual operation, which is inefficient, subjective, and difficult to meet modern requirements the need for intensive farming. This article describes a poultry health detection system based on Stacking multi model fusion technology. Through the fusion of deep learning models such as CNN, ResNet, AlexNet, and VGG, the accuracy, real-time performance, and lightweight performance of poultry manure image classification have been significantly improved. The experimental results show that the overall accuracy of the system is 0.9919, which is better than a single model and verifies the effectiveness of multi model integration. The article also analyzes the strategy of resource utilization in the poultry farming industry based on monitoring results. Technical support enables the intelligent and sustainable development of the poultry farming industry. In addition, the system can be promoted to other agricultural image analysis scenarios to achieve widespread application.
