综合考虑自动引导车(AGV)视觉导引控制的技术要求和特点,确定了AGV视觉导引控制系统图像处理的流程,并根据该流程依次分析了图像预处理、图像分割和特征提取的特点和要求,确定了适合AGV视觉导引控制的图像处理算法,以V isua l C++为软...综合考虑自动引导车(AGV)视觉导引控制的技术要求和特点,确定了AGV视觉导引控制系统图像处理的流程,并根据该流程依次分析了图像预处理、图像分割和特征提取的特点和要求,确定了适合AGV视觉导引控制的图像处理算法,以V isua l C++为软件开发平台,实现AGV视觉导引控制的计算机图像处理系统,为图像处理技术在AGV视觉导引控制中的应用提供一个较为简单而可行的方法。展开更多
The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence...The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)technologies.This transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and sustainability.This survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision agriculture.By providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural environments.The focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming tasks.This paper first discusses various salient crop metrics used in digital agriculture.Then this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and planning.After establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.展开更多
文摘综合考虑自动引导车(AGV)视觉导引控制的技术要求和特点,确定了AGV视觉导引控制系统图像处理的流程,并根据该流程依次分析了图像预处理、图像分割和特征提取的特点和要求,确定了适合AGV视觉导引控制的图像处理算法,以V isua l C++为软件开发平台,实现AGV视觉导引控制的计算机图像处理系统,为图像处理技术在AGV视觉导引控制中的应用提供一个较为简单而可行的方法。
基金supported in part by the United States Department of Agriculture(USDA)National Institute of Food and Agriculture(NIFA)Award Number 2023-67021-40614.
文摘The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)technologies.This transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and sustainability.This survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision agriculture.By providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural environments.The focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming tasks.This paper first discusses various salient crop metrics used in digital agriculture.Then this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and planning.After establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.
