Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Series Vol. 2 , 22 March 2023


Open Access | Article

Tomato leaf diseases classification using alexnet

D. Deepa * 1 , R. Yaswanth 2 , K. Vasantha Kumar 3
1 Department of Computer Science and Engineering College, Tamilnadu,
2 Department of Computer Science and Engineering College, Tamilnadu,
3 Department of Computer Science and Engineering College, Tamilnadu,

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 2, 635-642
Published 22 March 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation D. Deepa, R. Yaswanth, K. Vasantha Kumar. Tomato leaf diseases classification using alexnet. ACE (2023) Vol. 2: 635-642. DOI: 10.54254/2755-2721/2/20220627.

Abstract

The economy is largely reliant on agricultural productivity. Plant diseases and pests are also a serious issue in agriculture. It is necessary to recognise them early on in order to eliminate all infections as rapidly as possible and avoid crop devastation. To protect the plants against illness, a variety of insecticides have been utilised. Despite these precautions, the illness continues to spread throughout the field. Because we don't always know what kind of sickness we're dealing with, a bad pesticide may have been applied instead. As a result, it's all futile.Tomato leaf infections, on the other hand, are a severe problem for many farmers, thus mastering the severity of diseases in a fast and precise manner is critical to assisting staff in taking additional intervention steps to prevent plants from becoming more afflicted. An Alexnet model for detecting tomato leaf disease is proposed in this study, which may minimise the number of training processes while enhancing computation accuracy and gradient flow. This indicates that categorization of diseases is equally important. An improved classification model is presented in this paper for identifying and categorising tomato leaf disease. Before CNN is used to identify the pictures, a training dataset containing a large number of images is utilised, and visual characteristics are extracted using several methods. The Alexnet model achieves the highest classification accuracy when compared to other models.

Keywords

Extraction, Data Augmentation, Alexnet, Detection, Convolutional Neural Network

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Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
ISBN (Print)
978-1-915371-19-5
ISBN (Online)
978-1-915371-20-1
Published Date
22 March 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/2/20220627
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated