Tomato Leaf Disease Detection and Classification Using Cnn

Authors

  • Pushpa B R, Aiswarya V V

DOI:

https://doi.org/10.17762/msea.v71i4.853

Abstract

Tomatoes are a major crop of vegetables that are used in various foods. Plant diseases are currently the main challenge to food security, and researchers are attempting to develop an effective approach to identify and diagnose disease at their earliest stage, which aids in prevention of those diseases and thereby improves the agricultural sector. The dataset used in this research is a compilation of all publicly accessible datasets from Kaggle. used 14,531 datasets in 10 different classes. This study suggests an effective CNN (Convolutional Neural Network) model to categorize tomato leaf diseases and detect the name of the disease affecting tomato leaves. A 2-Dimensional Convolutional Neural Network model approach with 2-Max Assembling covers and a fully associated layer When compared to other classification models like SVM, VGG16, Inception V3 and Mobile Net CNN model, the experimental findings reveal that the model is effective enough to detect the disease with an accuracy of 96 %.

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Published

2022-09-19

How to Cite

Pushpa B R, Aiswarya V V. (2022). Tomato Leaf Disease Detection and Classification Using Cnn. Mathematical Statistician and Engineering Applications, 71(4), 2921–2930. https://doi.org/10.17762/msea.v71i4.853

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Section

Articles