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

• Published in: Artificial intelligence in agriculture Vol. 13; pp. 64 - 83
• Main Authors: Ghazal, Sumaira, Munir, Arslan, Qureshi, Waqar S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: agriculture; artificial intelligence; automation; Computer vision; Digital agriculture; economic development; food security; Image analysis; photogrammetry; precision agriculture; Smart agriculture; surveys; Vision-guided intelligent systems; Smart agriculture; Computer vision; Image analysis; Vision-guided intelligent systems; Digital agriculture
• ISSN: 2589-7217; 2589-7217
• DOI: 10.1016/j.aiia.2024.06.004

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. •Visual representation of a digital agriculture pipeline.•Overview of the digital life cycle of crops in precision agriculture.•Role of computer vision in transformation of traditional farming practices.