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ARS Home » Research » Publications at this Location » Publication #123700
Title: MACHINE VISION TECHNOLOGY FOR AGRICULTURAL APPLICATIONS

Author
item Chen, Yud
item Chao, Kuanglin - Kevin Chao
item Kim, Moon

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/18/2001
Publication Date: 11/1/2002
Citation: CHEN, Y.R., CHAO, K., KIM, M.S. MACHINE VISION TECHNOLOGY FOR AGRICULTURAL APPLICATIONS. COMPUTERS AND ELECTRONICS IN AGRICULTURE. 2002.

Interpretive Summary: Machine vision technology has the potential to become very important to the agricultural industry. The use of machine vision technology for land-based and aerial-based remote sensing for natural resources assessments, precision farming, postharvest product quality and safety detection, classification and sorting, and process automation will become routine in the near future. This paper briefly reviews current applications of machine vision in agriculture. It also discusses the requirements and recent developments of hardware and software for machine vision systems, with emphasis on multispectral and hyperspectral imaging for modern food engineering. Examples of applications for detection of disease, defects, and contamination on poultry carcasses and apples are also given. It also discusses future trends of machine vision technology applications in agriculture. This review article will be of great interest to scientists and technical managers who want to gain some knowledge of machine vision technology in agricultural applications and to engineers who are applying machine vision technology to solve agricultural problems.

Technical Abstract: Current applications of machine vision in agriculture are briefly reviewed. The requirements and recent developments of hardware and software for machine vision systems are discussed, with emphasis on multispectral and hyperspectral imaging for modern food engineering. Examples of applications for detection of disease, defects, and contamination on poultry carcasses and apples are also given. Future trends of machine vision technology applications are discussed.