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2023 | OriginalPaper | Chapter

Comparative Study of Tomato Crop Disease Detection System Using Deep Learning Techniques

Authors : Priya Ujawe, Smita Nirkhi

Published in: Intelligent Communication Technologies and Virtual Mobile Networks

Publisher: Springer Nature Singapore

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Abstract

Agriculture is the most important element of any country in several ways. The growth in agriculture helps to improve the country’s economy. Today, AgriTech is a growing field in the world that helps to improve the crop quality and quantity. Using different advanced techniques, farmers can be benefited. So many challenges are faced by the farmers during crop production. Crop disease is one of the most difficult obstacle of agriculture field. Many advanced techniques such as deep learning methods have been introduced to detect the crop diseases. Some convolutional neural network (CNN) architectures used for tomato crop disease detection are discussed in this paper. Comparative study of different CNN models like AlexNet, GoogleNet, ResNet, UNet, and SqueezNet has been performed.

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Title: Comparative Study of Tomato Crop Disease Detection System Using Deep Learning Techniques
Authors: Priya Ujawe
Smita Nirkhi
Publisher: Springer Nature Singapore
Book: Intelligent Communication Technologies and Virtual Mobile Networks
Print ISBN: 978-981-19-1843-8

Electronic ISBN: 978-981-19-1844-5

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-19-1844-5_39

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