Diseases Detection in Cotton Faring Using Deep Learning

DOI

10.2991/978-94-6463-471-6_126How to use a DOI?

Keywords

MLP; Random forest; Sequential CNN and Cotton Disease detection

Abstract

The primary crop in the world is the cotton, it is widely grown. India's very important profit crops and fibers, the nation’s agricultural and industrial depend on cotton. It provides The Cotton fiber, a basic raw ingredient, to the cotton textile industry. In India, six million farmers rely on the cotton field for their living, while between 50–60 million people work in cotton trade and processing. Several plant related illnesses, the yield and productivity are greatly reduced as a result Finding the illness at an early stage is big challenging. The deep leaning method was applied to provide a classification suggestion for cotton plant. Sequential CNN model was used to focus on most real-object identification system for classifying and detecting corrupted images and diseased and it give 90% accuracy.

Copyright

© 2024 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  - A. Mohan
AU  - P. Chiranjeevi
AU  - A. Krishna Mohan
PY  - 2024
DA  - 2024/07/30
TI  - Diseases Detection in Cotton Faring Using Deep Learning
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 1315
EP  - 1324
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_126
DO  - 10.2991/978-94-6463-471-6_126
ID  - Mohan2024
ER  -