Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)

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Analysis of Crop Diseases Using IoT and Machine Learning Approaches

Authors
Apeksha R. Gawande1, *, Swati S. Sherekar1
1Department of Computer Science and Engineering, SGB Amravati University, Amravati, Maharashtra, India
*Corresponding author. Email: apekshagawande@gmail.com
Corresponding Author
Apeksha R. Gawande
Available Online 1 May 2023.
DOI
10.2991/978-94-6463-136-4_10How to use a DOI?
Keywords
Plants diseases; Internet of Things; IoT; disease prediction; Support vector machine; Random forest
Abstract

For agricultural and farming practices to be more productive and cost-effective, it is imperative that the implementation of new technologies such as the Internet of Things (IoT) and Machine Learning be strongly considered in order to improve methods and procedures. In keeping with the evolution of agriculture, disease control measures have also evolved. Now a days, disease in plants can be undoubtedly identified using computers. Climate condition can be assessed for timely diagnosis and precise detection of crop diseases in order to control these diseases at an early stage. In order to prevent plant diseases from attacking, it is imperative that solutions are developed for the early prediction of disease attacks. An existing approach to disease detection uses computer vision, which detects diseases after they have already developed. The objective of this paper is to provide an insight into newly developed Internet of Things (IoT) applications in the agricultural sector, with a focus on sensor data collection and early detection of diseases.

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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Volume Title
Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
Series
Advances in Computer Science Research
Publication Date
1 May 2023
ISBN
10.2991/978-94-6463-136-4_10
ISSN
2352-538X
DOI
10.2991/978-94-6463-136-4_10How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Apeksha R. Gawande
AU  - Swati S. Sherekar
PY  - 2023
DA  - 2023/05/01
TI  - Analysis of Crop Diseases Using IoT and Machine Learning Approaches
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 78
EP  - 85
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_10
DO  - 10.2991/978-94-6463-136-4_10
ID  - Gawande2023
ER  -