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Plant Classification for Field Robots: A Machine Vision Approach

Plant Classification for Field Robots: A Machine Vision Approach

Sebastian Haug, Jörn Ostermann
Copyright: © 2016 |Pages: 25
ISBN13: 9781466694354|ISBN10: 1466694351|EISBN13: 9781466694361
DOI: 10.4018/978-1-4666-9435-4.ch012
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MLA

Haug, Sebastian, and Jörn Ostermann. "Plant Classification for Field Robots: A Machine Vision Approach." Computer Vision and Pattern Recognition in Environmental Informatics, edited by Jun Zhou, et al., IGI Global, 2016, pp. 248-272. https://doi.org/10.4018/978-1-4666-9435-4.ch012

APA

Haug, S. & Ostermann, J. (2016). Plant Classification for Field Robots: A Machine Vision Approach. In J. Zhou, X. Bai, & T. Caelli (Eds.), Computer Vision and Pattern Recognition in Environmental Informatics (pp. 248-272). IGI Global. https://doi.org/10.4018/978-1-4666-9435-4.ch012

Chicago

Haug, Sebastian, and Jörn Ostermann. "Plant Classification for Field Robots: A Machine Vision Approach." In Computer Vision and Pattern Recognition in Environmental Informatics, edited by Jun Zhou, Xiao Bai, and Terry Caelli, 248-272. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9435-4.ch012

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Abstract

Small size agricultural robots which are capable of sensing and manipulating the field environment are a promising approach towards more ecological, sustainable and human-friendly agriculture. This chapter proposes a machine vision approach for plant classification in the field and discusses its possible application in the context of robot based precision agriculture. The challenges of machine vision in the field are discussed at the example of plant classification for weed control. Automatic crop/weed discrimination enables new weed control strategies where single weed plants are treated individually. System development and evaluation are done using a dataset of images captured in a commercial organic carrot farm with the autonomous field robot Bonirob under field conditions. Results indicate plant classification performance with 93% average accuracy.

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