Author Login IJIRT Partner Network (IPN) Login

Rice Leaf Disease Detection using Machine Learning: A Review

  • Unique Paper ID: 173973
  • Volume: 11
  • Issue: 10
  • PageNo: 2189-2191
  • Abstract:
  • Rice is one of the most essential food crops worldwide, and its productivity is significantly affected by various diseases. Early detection and classification of rice leaf diseases can help prevent large-scale yield loss. This review paper provides a comparative study of various machine learning (ML) and deep learning (DL) approaches used for rice leaf disease detection. We analyze key methodologies, datasets, and performance metrics across multiple research studies. The findings indicate that deep learning models, particularly CNN-based architectures, achieve superior accuracy compared to traditional ML techniques. Furthermore, IoT and edge intelligence have emerged as promising trends for real-time disease monitoring in precision agriculture.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 2189-2191

Rice Leaf Disease Detection using Machine Learning: A Review

Related Articles

13 May, 2025

Rice Leaf Disease Detection using Machine Learning Algorithms

Read More
Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry