Deep Learning Models for Leaf Disease Detection for Crops in Agriculture Field : A Survey

Authors

  • Sunidhi Shrivastava  Department of Computer Science and Engineering, ITM University, Gwalior, Madhya Pradesh, India
  • Pankaj Gugnani  Department of Computer Science and Engineering, ITM University, Gwalior, Madhya Pradesh, India
  • Neha Garg  Department of Computer Science and Engineering, ITM University, Gwalior, Madhya Pradesh, India

DOI:

https://doi.org/10.32628/CSEIT20636

Keywords:

Deep learning, Plant leaf Disease Detection, Image processing, Machine Learning, Agriculture field.

Abstract

Crop and plant Diseases are the common problems in the food production fields. This is necessary for the improvement of the food production in agriculture and for fulfills the need of the society to solve these problems. In India most of the part of the country based on the production of food as a tradition. To solve these problems some advanced image processing, machine learning, computer vision etc. advancements included. This survey research on the identification of all that kind of technologies and the existing work also has done using them. How many kinds of models are proposed and what amount of success they have achieved by utilizing them. Image processing techniques provides the automatic disease detection technique to detect and identify the diseases in plants. Deep learning techniques are very good at prediction of the growth of plan and possibility of having disease within them. A comparison study also performed of several machine and deep learning techniques based on their accuracy.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2020-06-30

Issue

Volume 6, Issue 3, May-June-2020

Section

Research Articles

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How to Cite

[1]
Sunidhi Shrivastava, Pankaj Gugnani, Neha Garg, " Deep Learning Models for Leaf Disease Detection for Crops in Agriculture Field : A Survey" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.104-110, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT20636