Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 509263
Title Machine vision for a selective broccoli harvesting robot
Author(s) Blok, Pieter M.; Barth, Ruud; Berg, Wim Van Den
Source IFAC-PapersOnLine 49 (2016)16. - ISSN 2405-8963 - p. 66 - 71.
Event 5th IFAC Conference on Sensing, Control and Automation Technologies for Agriculture AGRICONTROL 2016, Seattle, WA, USA, Seattle, WA, 2016-08-14/2016-08-17
DOI https://doi.org/10.1016/j.ifacol.2016.10.013
Department(s) PPO/PRI AGRO Field Technology Innovations
WUR GTB Tuinbouw Technologie
Meteorology and Air Quality
Publication type Refereed Article in a scientific journal
Publication year 2016
Abstract The selective hand-harvest of fresh market broccoli is labor-intensive and comprises about 35% of the total production costs. This research was conducted to determine whether machine vision can be used to detect broccoli heads, as a first step in the development of a fully autonomous selective harvester. A texture and color based image segmentation was used to separate the broccoli head from the background. Segmentation results were compared to a ground truth dataset of 200 images. In these images, 228 broccoli heads of varying sizes were classified by two human experts with the GrabCut algorithm. Image segmentation was evaluated by two different metrics. The first was a pixel-based spatial overlap between the ground truth classification and image segmentation, which resulted an average overlap of 93.8%. The second metric was the individual broccoli head detection and the corresponding confusion matrix. These showed a precision score of 99.5%, indicating only one false positive. The specificity was 97.9%, negative predictive value was 69.7% and overall accuracy 92.4%. In total, 208 broccoli heads were detected by the machine vision software, indicating a sensitivity score of 91.2%. The average pixel size of the non-detected heads was smaller than the pixel size of the detected heads
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