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Transfer Learning and UNet Segmentation for Paddy Leaf Disease Classification as a Solution with a User-Friendly

Penugonda Seetha Rama Krishna1, S Nagarajan2

  • 1Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University.

Journal of Visualized Experiments : Jove
|November 10, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for identifying paddy leaf diseases, achieving high accuracy. The developed system aids farmers in early disease detection, improving crop yield and quality.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Plant Pathology

Background:

  • Paddy is a crucial global food crop, essential for economic stability in many nations.
  • Paddy cultivation faces significant threats from various leaf diseases, impacting yield and grain quality.
  • Accurate and timely disease identification is critical for effective crop management.

Purpose of the Study:

  • To develop and evaluate a deep learning model for accurate paddy leaf disease classification.
  • To enhance model performance through image segmentation techniques.
  • To create a user-friendly interface for practical application by farmers.

Main Methods:

  • A customized deep learning approach using transfer learning was implemented.
  • Six distinct models were evaluated, with DenseNet-121 showing superior performance.
  • Image segmentation using the UNet model was employed to improve dataset quality and model accuracy.

Main Results:

  • The tailored DenseNet-121 model achieved high performance metrics: 0.98 accuracy, 0.97 precision, and 0.96 recall.
  • Image segmentation significantly improved the accuracy of all evaluated models.
  • A graphical interface was developed for accessible paddy leaf disease identification.

Conclusions:

  • Deep learning, particularly the DenseNet-121 model with UNet segmentation, offers a dependable and scalable solution for paddy leaf disease classification.
  • The developed system can assist in early disease detection, contributing to improved agricultural practices and food security.
  • The user-friendly interface promotes the adoption of advanced technology in farming communities.