Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Implementation of Digital Twin Technology for predictive Crop Disease Monitoring

Authors
Sayali Vanjare1, *, Jitendra Musale1, Pranjali More1, Tuba Sayyad1, Yashita Gupta1, Sphurti Shikre1
1Anantrao Pawar College of Engineering and Research, Department of Computer Engineering, Pune, India
*Corresponding author. Email: sayalimvanjare@gmail.com
Corresponding Author
Sayali Vanjare
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_49How to use a DOI?
Keywords
Machine-learning; CNN; Digital-Twin; precision farming; early prediction; Azure platform
Abstract

With the population of animals and humans cultivating rates of crops and plants are increasing. Thus, demand also increases. The science of agriculture innovates several techniques to improve the cultivating sector to improve production. When it comes to production there is harvesting losses are considered. During cultivating farmers face problems like diseases and insects which aid production rate decreases and increase the rate of those crops. For several years science agriculture tried to make a quick medication system for detecting plant disease. For this problem, we produce a solution that features a combination of hardware and software-created models that help to predict diseases. Crops like potatoes and tomatoes are an everyday need in Indian homes. From few years rate of potato and tomato are goes high. To prevent these losses and deal with this problem this paper uses the digital twin concept which creates a replica of a farm and which works with Yolo to cure.

Copyright
© 2025 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 Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_49How to use a DOI?
Copyright
© 2025 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  - Sayali Vanjare
AU  - Jitendra Musale
AU  - Pranjali More
AU  - Tuba Sayyad
AU  - Yashita Gupta
AU  - Sphurti Shikre
PY  - 2025
DA  - 2025/05/26
TI  - Implementation of Digital Twin Technology for predictive Crop Disease Monitoring
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 654
EP  - 665
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_49
DO  - 10.2991/978-94-6463-716-8_49
ID  - Vanjare2025
ER  -