Applied machine vision of plants: a review with implications for field deployment in automated farming operations
Article
Article Title | Applied machine vision of plants: a review with implications for field deployment in automated farming operations |
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ERA Journal ID | 124822 |
Article Category | Article |
Authors | McCarthy, C. L. (Author), Hancock, N. H. (Author) and Raine, S. R. (Author) |
Journal Title | Intelligent Service Robotics |
Journal Citation | 3 (4), pp. 209-217 |
Number of Pages | 9 |
Year | 2010 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1861-2776 |
1861-2784 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11370-010-0075-2 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11370-010-0075-2 |
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. |
Keywords | monocular vision; stereo vision; range sensing; plant sensing; multispectral |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460304. Computer vision |
400799. Control engineering, mechatronics and robotics not elsewhere classified | |
300403. Agronomy | |
460306. Image processing | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | National Centre for Engineering in Agriculture |
https://research.usq.edu.au/item/q0018/applied-machine-vision-of-plants-a-review-with-implications-for-field-deployment-in-automated-farming-operations
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