IDENTIFICATION OF TOMATO LEAF DISEASE PREDICTION USING CNN

Niharika Saxena; Dr. Neha  Sharma

doi:10.29121/ijoest.v6.i5.2022.397

Authors

Niharika Saxena Student, Electronics and Communication, Ujjain Engineering College, Ujjain, M.P., India
Dr. Neha Sharma Professor, Electronics and Communication, Ujjain Engineering College, Ujjain, M.P., India

DOI:

https://doi.org/10.29121/ijoest.v6.i5.2022.397

Keywords:

Artificial Intelligence, Convolutional Neural Network (CNN), Deep Learning, Leaf Disease, Tomato Leaf, Multiclass Classification

Abstract

In India tomatoes are broadly vegetable crop. However, the tropical environment is ideal for tomato plant growth, specific climatic conditions and other factors influence tomato plant growth. Aside from these environmental factors and natural disasters, plant disease is a serious agricultural production issue that causes economic loss. As an outcome, early illness detection may produce better results than existing detection methods. As a result, deep learning approaches based on computer vision might be used to detect diseases early. The disease categorization and detection strategies used to identify tomato leaf diseases are thoroughly examined in this study. This study also discusses the benefits and drawbacks of the approaches presented. After all, using a hybrid deep-learning architecture, this study provides an early disease detection method for tomato leaf disease.

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