Regular Issue - 3
One of the world's largest exporters of tea is India. However, persistent pathogen exposure-related tea leaf diseases cause significant global crop output losses. Early detection of the tea leaf disease can lessen its detrimental effects on tea production. It might be ineffective and harmful to diagnose the illness with the unaided eye. Convolutional Neural Networks (CNNs) are frequently employed to apply an efficient technique for the classification of images. CNN is frequently used in plant disease detection. As a result, the suggested work considers using a Deep CNN with many hidden layers to classify damaged tea leaves into various groups. This aids the network in identifying more characteristics, increasing the precision of illness identification. The following leaf classifications are used for the classification process: Red Spot, Heliopolis, Brown Blight, Algal Spot, Gray Blight, and Healthy Leaves. Additionally, 5867 photos of healthy and diseased tea leaves have been tagged and posted to Kaggle. The proposed approach shows that the model has a 96.56% accuracy rate in identifying the type of persistent tea leaf disease. The following illness classes have the results show that Algal Spot has an accuracy of 98.23%, Brown Blight has an accuracy of 97.98%, Gray Blight has an accuracy of 93.46%, the Heliopolis disease class has an accuracy of 98.98%, and Red Spot has an accuracy of 92% accuracy In terms of accuracy, the model put forward in this literature is significantly better than the current approaches.
100
75
43
100
75
43
100
75
43
India
""""
Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: N.Rama Kumar, 22hr1a0206@mtieat.org
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
No. of Readers
No. of Reaction
No. of Comments
No. of Downloads
Or share your Opinion
Convolutional Neural Networks (CNNs) have currently acquired focus from researchers to address computer vision and medical image processing...
This project focuses on the development of an adaptive vest for soldier health monitoring and temperature control. The...
The high-speed carry look ahead adder (CLA), however, has become a very powerful tool for carrying out addition...
This paper presents the design and implementation of an Enhanced Reversible Logic Gates-Based Pipelined Arithmetic Logic Unit (ALU)...
The diagnosis of kidney disease, often called chronic renal illness, is known as chronic kidney disease (CKD). Chronic...
Impact Factor
Scopus & Web of Science under Evalution
Comments(0)