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

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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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  1. National Centre for Engineering in Agriculture, Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD, 4350, Australia

    C. L. McCarthy, N. H. Hancock & S. R. Raine

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McCarthy, C.L., Hancock, N.H. & Raine, S.R. Applied machine vision of plants: a review with implications for field deployment in automated farming operations. Intel Serv Robotics 3, 209–217 (2010). https://doi.org/10.1007/s11370-010-0075-2

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