Computer vision in smart agriculture and precision farming:Techniques and applications  

作　　者：Sumaira Ghazal Arslan Munir Waqar S.Qureshi 

机构地区：[1]Department of Computer Science,Kansas State University,66506 Manhattan,KS,USA [2]Department of Electrical Engineering and Computer Science,Florida Atlantic University,33431 Boca Raton,FL,USA [3]School of Computer Science,University of Galway,H91 TK33 Galway,Ireland

出　　处：《Artificial Intelligence in Agriculture》2024年第3期64-83,共20页农业人工智能（英文）

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.

摘　　要：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.

关 键 词：Digital agriculture Computer vision Smart agriculture Image analysis Vision-guided intelligent systems 

分 类 号：S24[农业科学—农业电气化与自动化]