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A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00007600
URN to cite this document: urn:nbn:de:bvb:703-epub-7600-0

Title data

Farah, Niklas ; Drack, Nicolas ; Dawel, Hannah ; Büttner, Ricardo:
A Deep Learning-Based Approach for the Detection of Infested Soybean Leaves.
In: IEEE Access. Vol. 11 (2023) . - pp. 99670-99679.
ISSN 2169-3536
DOI der Verlagsversion: https://doi.org/10.1109/ACCESS.2023.3313978

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Project information

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Open Access Publizieren
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Project financing: Deutsche Forschungsgemeinschaft

Further data

Item Type: Article in a journal
Keywords: Crops; Biological system modeling; Production; Pesticides; Economics; Deep learning; Autonomous aerial vehicles; Convolutional neural networks; Plants (biology); Plant diseases; Convolutional neural network; VGG-19; plant infestation; soybean; Diabrotica speciosa; caterpillars
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
Institutions of the University: Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science > Chair Business Administration XVIII - Information Systems Management and Data Science - Univ.-Prof. Dr. Ricardo Büttner
Faculties
Faculties > Faculty of Law, Business and Economics
Faculties > Faculty of Law, Business and Economics > Department of Business Administration
Faculties > Faculty of Law, Business and Economics > Department of Business Administration > Chair Business Administration XVIII - Information Systems Management and Data Science
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-7600-0
Date Deposited: 20 Mar 2024 07:09
Last Modified: 20 Mar 2024 07:10
URI: https://epub.uni-bayreuth.de/id/eprint/7600

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