Title: Vine Leaf Disease and AI, Detection of grapevine leaf diseases based on RGB-images, deep learning and its integration in a mobile application
Authors: Licka, Maria-Theresa, Elisabeth-von-Thadden-Schule, Heidelberg
Schweikert, Mario, Leibniz-Gymnasium, Neustadt an der Weinstraße
Contributors: Editor: Physikalisch-Technische Bundesanstalt (PTB), ISNI: 0000 0001 2186 1887
HostingInstitution: Physikalisch-Technische Bundesanstalt (PTB), ISNI: 0000 0001 2186 1887
Pages:16
Language:en
DOI:10.7795/320.202210
Resource Type: Text / Article
Publisher: Physikalisch-Technische Bundesanstalt (PTB)
Rights: Vervielfältigung nur zum eigenen persönlichen Gebrauch.
Dates: Available: 2023-03-16
Accepted: 2022-06-20
Submitted: 2022-02-04
File: Download File (application/pdf) 5.15 MB (5402223 Bytes)
MD5 Checksum: d11ddf3532651e8f83133066a1b19b29
SHA256 Checksum: a1b1a821a128f236d35c25258e2b9b03fcd5a709ce33cd3c556c546e1c2b5d6c
Keywords Grapevine leaf ; Disease ; Smartphone App ; Machine learning ; Convolutional Neural Network ; Python ; Tensorflow ; Pesticide usage ; Agriculture ; Sustainability
Abstract: This project enables early grapevine leaf disease identification on grape leaves by cell phone images, thereby allowing a precise usage of pesticides. The application is based on artificial intelligence (AI) which is trained to detect and differentiate the most common diseases. A continuous update of the extent and geographical location of disease spreading gives further valuable information to the winemakers using the application.
Series Information: Junge Wissenschaft. Paper 10/2022
Other: In der Jungen Wissenschaft werden Forschungsarbeiten von Schüler/innen, die selbstständig, z.B. in einer Schule oder einem Schülerforschungszentrum, durchgeführt wurden, veröffentlicht.
Citation: Licka, Maria-Theresa ; Schweikert, Mario . Vine Leaf Disease and AI. Detection of grapevine leaf diseases based on RGB-images, deep learning and its integration in a mobile application. Physikalisch-Technische Bundesanstalt (PTB), 2022. DOI: https://doi.org/10.7795/320.202210