One-Class Learning Weed Plants Detection on Multispectral Images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146892" target="_blank" >RIV/00216305:26220/22:PU146892 - isvavai.cz</a>
Alternative codes found
RIV/62156489:43210/22:43922567
Result on the web
<a href="https://ieeexplore.ieee.org/document/9943391" target="_blank" >https://ieeexplore.ieee.org/document/9943391</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT57764.2022.9943391" target="_blank" >10.1109/ICUMT57764.2022.9943391</a>

Result language
angličtina
Original language name
One-Class Learning Weed Plants Detection on Multispectral Images
Original language description
Modern precision agriculture methods focus on efficient crop care procedures with targeted chemical applications. As current computer vision algorithms allow us to distinguish different plant species, spot-spraying systems for precise herbicide application only on weed plants are being developed and used in praxis. Many such systems rely on deep learning algorithms trained on large datasets containing all possible plant species. While the crop plants keep the same visual characteristics all around the world, the species composition of weed plants can differ significantly, leading to lower detection accuracy for weed species that are not represented in the training dataset. In this work, we apply the PatchCore and PaDiM, two state-of-the-art anomaly detection algorithms, to a multispectral dataset of corn plant images in a one-class learning paradigm. The best performing algorithm achieved AUROC 94.2 despite the high visual heterogeneity and scarcity of the input data. Our results suggest it is possible to train weed-detection algorithms on a limited dataset in a one-class learning setting to transform the species classification into an anomaly detection problem.
Czech name
—
Czech description
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Project
<a href="/en/project/FW03010273" target="_blank" >FW03010273: Defectoscopy of painted parts using automatic adaptation of neural networks</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2022 14th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
ISBN
979-8-3503-9866-3
ISSN
2157-023X
e-ISSN
—
Number of pages
4
Pages from-to
76-79
Publisher name
IEEE
Place of publication
Valencia, Spain
Event location
Valencia, Spain
Event date
Oct 11, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

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