Leaf Disease Detection Using Deep Learning

DOI

10.2991/978-94-6463-136-4_87How to use a DOI?

Keywords

Convolutional neural network (CNN); Image Feature Extraction; Classification

Abstract

Leaf disease detection uses a technology that can identify the disease of a leaf or plant and, in response, provide the best solution to overcome the disease in order to provide us with an appropriate remedy that can be utilized as a defensive mechanism against the disease identified. Agriculture being a very significant part of many developing nations. As a result, it becomes essential to recognize infected plant leaves and categorize diseases in order to prevent serious plant loss. With faster and more precise answers, the loss of farmers can be avoided. There are four steps to determining the type of disease: picture preprocessing, feature extraction, classification, and diagnosis Convolution Neural Network (CNN), which consists of various layers utilized for prediction, is used for categorization and image preprocessing to enhance the quality of the image. A cure is advised for the user at the final stage.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jaya Jeswani
AU  - Ansari Saud Ahmed
AU  - Khan Zaid
AU  - Russel Fernandes
PY  - 2023
DA  - 2023/05/01
TI  - Leaf Disease Detection Using Deep Learning
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 989
EP  - 994
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_87
DO  - 10.2991/978-94-6463-136-4_87
ID  - Jeswani2023
ER  -