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 559709
Title Automatic Detection of Tulip Breaking Virus (TBV) Using a Deep Convolutional Neural Network
Author(s) Polder, Gerrit; Westeringh, Nick Van De; Kool, Janne; Khan, Haris Ahmad; Kootstra, Gert; Nieuwenhuizen, Ard
Source IFAC-PapersOnLine 52 (2019)30. - ISSN 2405-8963 - p. 12 - 17.
DOI https://doi.org/10.1016/j.ifacol.2019.12.482
Department(s) GTB Tuinbouw Technologie
GTB Bedrijfsbureau
Agro Field Technology Innovations
Farm Technology
PE&RC
Publication type Refereed Article in a scientific journal
Publication year 2019
Abstract Tulip crop production in the Netherlands suffers from severe economic losses caused by virus diseases such as the Tulip Breaking Virus (TBV). Infected plants which can spread the disease by aphids must be removed from the field as soon as possible. As the availability of human experts for visual inspection in the field is limited, there is an urgent need for a rapid, automated and objective method of screening. From 2009-2012, we developed an automatic machine-vision-based system, using classical machine-learning algorithms. In 2012, the experiment conducted a tulip field planted at production density of 100 and 125 plants per square meter, resulting in images with overlapping plants. Experiments based on multispectral images resulted in scores that approached results obtained by experienced crop experts. The method, however, needed to be tuned specifically for each of the data trails, and a NIR band was needed for background segmentation. Recent developments in artificial intelligence and specifically in the area of convolutional neural networks, allow the development of more generic solutions for the detection of TBV. In this study, a Faster R-CNN network is applied on part of the data from the 2012 experiment. The outcomes show that the results are almost the same compared to the previous method using only RGB data.
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