Deep learning image-based automated application on classification of tomato leaf disease by pre-trained deep convolutional neural networks

  • ReddyPriya Madupuri Department of Computer Science and Engineering, SRM University-AP, Amaravati, Guntur India
  • Dinesh Reddy Vemula Department of Computer Science and Engineering, SRM University-AP, Amaravati, Guntur India
  • Anil Carie Chettupally Department of Computer Science and Engineering, SRM University-AP, Amaravati, Guntur India
  • Abdur Rashid Sangi Department of Computer Science, College of Science and Technology, Wenzhou-Kean University, Ouhai, Wenzhou Zhejiang China
  • Pallam Ravi Department of Computer Science and Engineering, Anurag University, Hyderabad India

Abstract

The agriculture sector is one of the major sectors in India. India is well known for the production of various varieties of spices, fruits, vegetables, herbs, etc. Along with the pollution, the diseases that are affecting plants are increasing and there are various reasons for this. Tomato is one of the high-demand crops in the market and is produced in large quantities. There are many diseases that tomatoes get affected by because of the virus, fungus, bacteria, etc. In this project, we proposed a model to identify the diseases of tomato plants using images of tomato plant leaves. Our main goal is to develop a good model with decent accuracy and a mobile application that works with or without the internet for users, especially farmers. The Convolution Neural Network-based approach is used to create the model for this project. This proposed system model gives 98 % accuracy and that model is converted to the TF Lite model which is used in the application. This application can precisely predict the disease of the tomato leaf and suggest the treatment for it.

Published
Jul 21, 2023
How to Cite
MADUPURI, ReddyPriya et al. Deep learning image-based automated application on classification of tomato leaf disease by pre-trained deep convolutional neural networks. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 42, n. 3, p. 52-58, july 2023. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2739>. Date accessed: 19 july 2024. doi: http://dx.doi.org/10.22581/muet1982.2303.06.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License