Early Detection of Plant Leaf Disease Using Deep Learning

Authors

  • Shivam Ardalkar  Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Aishwarya Waghmode  Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Aniket Jadhav  Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Parimal Bhosale  Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India
  • Prof. Afsha Akkalkot  Computer Engineering, Zeal College of Engineering and Research, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/IJSRST2184939

Keywords:

Plant Leaf Disease, Deep Learning, CNN Algorithm, Image processing, Neural Network, Agriculture

Abstract

As crop diseases spread, farmers need to prevent and control their contamination to increase productivity and yield. In addition to affecting plants, plant diseases affect market access and agricultural production. As one of the most important vegetable plants in the world, leaf images can be used to classify diseases early. In addition to Yellow Curved, Late Blight, and Leaf Spot, Bacterial leaf Spot, there are other diseases of plants. The dataset is taken from the Internet. In this paper, we take leaf images with various leaf diseases and train it with the best model Convolutional Neural Network (CNN). We then use the weights from CNN to test new leaf images. By using convolutional neural networks (CNNs) and deep learning techniques, we have proposed a system that classifies plant leaf diseases. With the hybrid approach, the model will demonstrate its accuracy. To identify the best model, performance analysis is done with accuracy and finds solution to the problem.

References

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Published

2023-04-30

Issue

Section

Research Articles

How to Cite

[1]
Shivam Ardalkar, Aishwarya Waghmode, Aniket Jadhav, Parimal Bhosale, Prof. Afsha Akkalkot "Early Detection of Plant Leaf Disease Using Deep Learning" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 10, Issue 2, pp.906-911, March-April-2023. Available at doi : https://doi.org/10.32628/IJSRST2184939