| Issue |
ITM Web Conf.
Volume 40, 2021
International Conference on Automation, Computing and Communication 2021 (ICACC-2021)
|
|
|---|---|---|
| Article Number | 03043 | |
| Number of page(s) | 5 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20214003043 | |
| Published online | 09 August 2021 | |
LEAF DISEASE DETECTION OF CUCURBITS USING CNN
1 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
2 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
3 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
4 Department of Information Technology, Ramrao Adik Institute of Technology, Nerul, Navi Mumbai, India
Isha Agrawal: ishaagrawal2000@gmail.com
Prada Hegde: pahegde99@gmail.com
Pooja Shetty: p.shetty1406@gmail.com
Priyanka Shingane: priyankashingane@gmail.com
Identification of plant disease is tough in agribusiness arena. If it is inaccurate, there occurs a tremendous damage in the production and economical price. Leaf Disease detection requires huge amount of work, knowledge, processing time in plant disease. The most used and edible vegetable all over the world is from cucurbitaceous family. The crops under this family have great economic value in the food industry and its production is done in large scale. This family consists of 965 species. If any of these plants catch disease then there would be a tremendous loss in the production of this field yields. Thus, treating them at early stage is best way to prevent such losses. Hence, Deep Learning Algorithm like CNN can be used to detect the diseases of the plants. The leaves of the plants would be used as primary material for identification of the disease, as they are much more visible on the leaves.
Key words: Leaf Disease Detection / Cucurbitaceae family / CNN / Deep Learning
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.