Digital Twin-Driven Crop Growth Monitoring System Integrated with Deep Learning-Based Disease and Quality Assessment

DOI

10.2991/978-94-6239-713-2_22How to use a DOI?

Keywords

Digital Twin; Deep Learning; Computer Vision; Crop Growth Monitoring; Disease Detection; Digital Agriculture

Abstract

Recent advances in artificial intelligence have led to the development of systems that can interpret visual agricultural data and support decision-making at various stages of crop growth. This paper presents an integrated plant intelligence framework that combines structured JSON-based crop knowledge modeling, deep learning-based visual analytics, and a digital twin-driven method for tracking growth over time, all within a single mobile application. The system workflow starts with user-captured crop images. These images undergo preprocessing and are analyzed using deep learning architectures, including Custom Convolutional Neural Networks (CNN), EfficientNet, and ResNet, which have been tested experimentally. The analysis focuses on growth stage classification, disease detection, and fruit quality assessment. The predicted outputs are combined with user-provided planting timelines and specific agronomic growth rules. This integration creates a dynamic digital twin representation of crop development, allowing for visualization and monitoring throughout the lifecycle. Experimental evaluations show that the ResNet-based disease detection model achieved a classification accuracy of 92%, better than CNN at 78% and EfficientNet at 91%. The fruit quality assessment model reached a peak accuracy of 98%. These results demonstrate the effectiveness of pairing vision-based prediction with growth simulation to improve understanding and practical decision making. The modular design of the framework allows it to scale across various crops. It also supports real-world agricultural monitoring through an easy-to-use mobile interface.

Copyright

© 2026 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  - Avighnaa Thirumaran
AU  - R. Raja Subiksha
AU  - N. Umamageshwari
AU  - S. Alagammal
PY  - 2026
DA  - 2026/06/25
TI  - Digital Twin-Driven Crop Growth Monitoring System Integrated with Deep Learning-Based Disease and Quality Assessment
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 302
EP  - 313
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_22
DO  - 10.2991/978-94-6239-713-2_22
ID  - Thirumaran2026
ER  -