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Rice Leaf Disease Detection using Machine Learning Algorithms

  • Unique Paper ID: 178976
  • Volume: 11
  • Issue: 12
  • PageNo: 4949-4952
  • Abstract:
  • Rice is one of the most essential staple crops globally, and its yield is significantly threatened by various leaf diseases, which can lead to major economic losses if not detected and managed in time. Traditional manual methods of disease detection are often time-consuming, error-prone, and require expert knowledge. In this research, we propose an automated approach for the early detection and classification of rice leaf diseases using machine learning algorithms. High-resolution images of rice leaves affected by various diseases such as bacterial leaf blight, brown spot, and leaf smut were collected and preprocessed through resizing, normalization, and feature extraction. The dataset was then used to train and evaluate multiple classification algorithms including Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN). Experimental results demonstrated that the SVM-RF hybrid model achieved the highest accuracy, outperforming individual models. This study highlights the potential of machine learning techniques in building an efficient and scalable solution for rice disease diagnosis, which could assist farmers and agronomists in timely intervention and improve overall crop productivity
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 4949-4952

Rice Leaf Disease Detection using Machine Learning Algorithms

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

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