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Applied machine vision of plants: a review with implications for field deployment in automated farming operations
Author(s) -
Cheryl McCarthy,
N.H. Hancock,
Steven R. Raine
Publication year - 2010
Publication title -
intelligent service robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.398
H-Index - 30
eISSN - 1861-2784
pISSN - 1861-2776
DOI - 10.1007/s11370-010-0075-2
Subject(s) - computer science , precision agriculture , software deployment , machine vision , artificial intelligence , robustness (evolution) , rgb color model , image processing , machine learning , computer vision , agriculture , image (mathematics) , software engineering , gene , biology , ecology , biochemistry , chemistry
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.\u

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