Tomato Leaf Disease Detection and Classification Using Convolutional Neural Network and Machine Learning

Clicks: 1
ID: 313022
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #530 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Tomato is one of the most important horticulture crops in the world, however, it faces many important threats due to different diseases which may deteriorate the yield quality and loss. For management of efficient crop and reasonable yield, rapid and precise disease identification is very important. This study has made elaborate comparison between the conventional machine learning and deep learning for the classification of Tomato leaves images. This dataset has a total of 30000 images of 10 different types of diseases and healthy condition of Tomato leaves. The segmentation has been made for each disease as Bacterial-Spot (BS), Early-Blight (EB), Healthy, Late-Blight (LB), Leaf-Mold (LM), Septoria-Leaf-Spot (SLS), Spider-Mites-Two-spotted-Spider-Mite (SMTSSM), Target-Spot (TS), Tomato-Mosaic-Virus (TMV) and Tomato-Yellow-Leaf-Curl-Virus (TYLCV). A Convolutional Neural Network (CNN) is applied which consists of four convolutional layers, max pooling, a fully connected layer, dropout for regularization and a softmax layer for class probability. The classic machine learning techniques such as adaBoost classifier (ABC), K-nearest neighbor (KNN), random forest classifier (RFC), and naïve bayes (NB) has also been examined. Our experiment showed that the CNN is significantly better than the classic classifiers, it was able to able to obtain 96% accuracy whereas the classic classifiers gave poor accuracy, ABC 52%, KNN 56%, RFC 71% and NB 49%.
Reference Key
imported_1777058516_69ebc2d480b1c Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohammad Nasir
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/348
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.