Applied machine vision of plants: a review with implications for field deployment in automated farming operations

McCarthy, Cheryl and Hancock, Nigel and Raine, Steven R. (2010) Applied machine vision of plants: a review with implications for field deployment in automated farming operations. Intelligent Service Robotics, 3 (4). pp. 209-217. ISSN 1861-2776

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Abstract

Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight. From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Authors' Accepted Version deposited in accordance with the copyright policy of the publisher (Springer).
Depositing User: Ms Cheryl McCarthy
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - No Department
Date Deposited: 11 Dec 2010 08:24
Last Modified: 03 Jul 2013 00:00
Uncontrolled Keywords: monocular vision; stereo vision; range sensing; plant sensing; multispectral
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision
09 Engineering > 0913 Mechanical Engineering > 091302 Automation and Control Engineering
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070302 Agronomy
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objective (SEO2008): B Economic Development > 86 Manufacturing > 8614 Machinery and Equipment > 861401 Agricultural Machinery and Equipment
Identification Number or DOI: doi: 10.1007/s11370-010-0075-2
URI: http://eprints.usq.edu.au/id/eprint/8567

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