Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach

Utkarsh Yashwant Tambe, A. Shobanadevi, A. Shanthini, Hsiu-Chun Hsu
11/4/2023
cs.CVcs.AIcs.LG

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Abstract

In this study, a Convolutional Neural Network (CNN) is used to classify potato leaf illnesses using Deep Learning. The suggested approach entails preprocessing the leaf image data, training a CNN model on that data, and assessing the model's success on a test set. The experimental findings show that the CNN model, with an overall accuracy of 99.1%, is highly accurate in identifying two kinds of potato leaf diseases, including Early Blight, Late Blight, and Healthy. The suggested method may offer a trustworthy and effective remedy for identifying potato diseases, which is essential for maintaining food security and minimizing financial losses in agriculture. The model can accurately recognize the various disease types even when there are severe infections present. This work highlights the potential of deep learning methods for categorizing potato diseases, which can help with effective and automated disease management in potato farming.

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Cite this paper

@article{tambe2023potato,
  title  = {Potato Leaf Disease Classification using Deep Learning: A Convolutional Neural Network Approach},
  author = {Utkarsh Yashwant Tambe and A. Shobanadevi and A. Shanthini and Hsiu-Chun Hsu},
  year   = {2023},
  eprint = {2311.02338},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.02338v1}
}

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