The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
A grapevine leaves dataset for early detection and classification of esca disease in vineyards through machine learning
Open accessAbstract
Esca is one of the most common disease that can severely damage grapevine. This disease, if not properly treated in time, is the cause of vegetative stress or death of the attacked plant, with the consequence of losses in production as well as a rising risk of propagation to the closer grapevines. Nowadays, the detection of Esca is carried out manually through visual surveys usually done by agronomists, requiring enormous amount of time. Recently, image processing, computer vision and machine learning methods have been widely adopted for plant diseases classification. These methods can minimize the time spent for anomaly detection ensuring an early detection of Esca disease in grapevine plants that helps in preventing it to spread in the vineyards and in minimizing the financial loss to the wine producers. In this article, an image dataset of grapevine leaves is presented. The dataset holds grapevine leaves images belonging to two classes: unhealthy leaves acquired from plants affected by Esca disease and healthy leaves. The data presented has been collected to be used in a research project jointly developed by the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy and the STMicroelectronics, Italy, under the cooperation of the Umani Ronchi SPA winery, Osimo, Ancona, Marche, Italy. The dataset could be helpful to researchers who use machine learning and computer vision algorithms to develop applications that help agronomists in early detection of grapevine plant diseases. The dataset is freely available at http://dx.doi.org/10.17632/89cnxc58kj.1.
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The pipeline in [117] allows users to create an embedded ML that can be used for different plants in labs, greenhouses, farms or gardens.
“This pipeline allows users to create an embedded ML that can be used for different plants in labs, greenhouses, farms or gardens.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [117]
The Augmentation procedure (page unavailable) describes code that users can configure to augment grapevine-leaf images and save a dataset. It does not say the code creates an embedded ML system or that it can be used for different plants in labs, greenhouses, farms, or gardens. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAuthors in [117] designed an embedded ML pipeline that helps farmers and scientists monitor the health of the crop and its growth.
“The authors in [117] designed an embedded ML pipeline that helps farmers and scientists to monitor the health of the crop and its growth.”
From A Comprehensive Survey on TinyML (2023) · cites this paper as [117]
The Value of the Data section (page unavailable) says the grapevine-leaf dataset can be used to develop embedded applications for early disease detection, and the Abstract says it could help researchers develop tools for agronomists. The paper does not say the authors designed an embedded ML pipeline or that it monitors crop growth. (LLM, conf=0.88)
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Cites
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Research works printed but not linked: 1
- Chollet, 2016, Building powerful image classification models using very little data, Keras Blog
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A Comprehensive Survey on TinyML
Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366
Falaschetti, L., Manoni, L., Rivera, R. C. F., Pau, D., Romanazzi, G., Silvestroni, O., Tomaselli, V., & Turchetti, C. (2021) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2021.3098454 · Cited by 45
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Metadata
- Authors
- Alessandrini, M., Calero Fuentes Rivera, R., Falaschetti, L., Pau, D., Tomaselli, V., & Turchetti, C.
- Affiliations
- Marche Polytechnic University (IT)
- STMicroelectronics (Italy) (IT)
- Corresponding author
- Falaschetti, L.
- Venue
- Data in Brief
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 35
- Pages
- 106809
- ISSN
- 2352-3409 (ISSN-L)
- PMC
- PMC7881216
- OpenAlex
- W3127631478
- Cited by
- 60
- References
- 12
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 19 · 2023: 9 · 2022: 7 · 2021: 5
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Data in Brief (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Università Politecnica delle Marche (Università Politecnica delle Marche) (submittedVersion, cc-by-nc-nd)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- Electronic Components and Systems for European Leadership
- Horizon 2020 Framework Programme
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- grapevine leaves, plant disease detection, early disease detection, leaf image dataset, vineyard disease management, plant disease classification, computer vision for agriculture
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