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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM40343
Volume:
8 |
12 |
Dec 2024
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.
Agriculture is vital to the Indian economy, with cash crops playing a significant role in rural income. Farmers face challenges such as pest infestations, crop diseases, and climate unpredictability, resulting in severe financial losses. This study introduces an integrated platform leveraging machine learning (ML), image processing, and web-based technologies to mitigate these issues. Modules include disease detection using Convolutional Neural Networks (CNN), crop yield prediction with Random Forest, and fertilizer recommendations using soil health parameters. These modules are integrated into a Django/flask-based web application, offering a user-friendly interface. Results demonstrate high accuracy and scalability, providing actionable insights to empower farmers. This initiative addresses agricultural challenges through data-driven solutions, promoting sustainable farming practices and improved productivity.
KEYWORDS
Publication Date
12/31/2024
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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No funding information available for this research.
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