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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM21080
Volume:
7 |
5 |
May 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
To address the issues facing agriculture, the Computer Vision Plant Diagnostics offers an innovative and practical solution. It is important to implement better farming methods and efficiently manage Plant diseases as the worldâs population continues to rise and puts strain on food production. This system makes use of the machineâs power. Using machine learning and image recognition, farmers, gardeners, and agricultural experts can now have access to a program that can accurately identify Plant illnesses. Users can rely on model within the system to identify the disease and suggest required measures for limiting its impact, by uploading pictures of plants or leafâs. Key-Words: PyTorch, Disease, Convolutional Neural Networks
KEYWORDS
Publication Date
4/15/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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