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 549180
Title Potato Virus Y Detection in Seed Potatoes Using Deep Learning on Hyperspectral Images
Author(s) Polder, G.; Blok, P.M.; Villiers, H.A.C. de; Wolf, J.M. van der; Kamp, J.A.L.M.
Source Frontiers in Plant Science 10 (2019). - ISSN 1664-462X
DOI https://doi.org/10.3389/fpls.2019.00209
Department(s) GTB Tuinbouw Technologie
Agro Field Technology Innovations
Post Harvest Technology
Biointeractions and Plant Health
OT Team Int. Prod. & Gewasinn.
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) crop resistance - Phenotyping - hyperspectral imaging - classification - Convolutional neural network - Solanum tuberosum
Abstract Virus diseases are of high concern in the cultivation of seed potatoes. Once found inthe field, virus diseased plants lead to declassification or even rejection of the seed lotsresulting in a financial loss. Farmers put in a lot of effort to detect diseased plants andremove virus-diseased plants from the field. Nevertheless, dependent on the cultivar,virus diseased plants can be missed during visual observations in particular in an earlystage of cultivation. Therefore, there is a need for fast and objective disease detection.Early detection of diseased plants with modern vision techniques can significantlyreduce costs. Laboratory experiments in previous years showed that hyperspectral imaging clearly could distinguish healthy from virus infected potato plants. This paper reports on our first real field experiment. A new imaging setup was designed, consisting of a hyperspectral line-scan camera. Hyperspectral images were taken in the field with a line interval of 5 mm. A fully convolutional neural network was adapted for hyperspectral images and trained on two experimental rows in the field. The trained network was validated on two other rows, with different potato cultivars. For three of the four row/date combinations the precision and recall compared to conventional disease assessment exceeded 0.78 and 0.88, respectively. This proves the suitability of this method for real world disease detection.
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