Joytu Debnath
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Joytu Debnath has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Ahad Ahmed Lasker
Assam University, Silchar, India
1 shared publication - Kundan Kumar
Assam University, Silchar, India
1 shared publication - Mrutyunjay Padhiary
Assam University, Silchar, India
1 shared publication - Priyanka Roy
Assam University, Silchar, India
1 shared publication
Latest Publications
- 7 May 2026
- Published by IGI Global Scientific Publishing
- p. 249-286
Digital Twins for Smart Crop, Soil, and Resource Optimization in Modern Agriculture
Modern agriculture is increasingly leveraging digital technologies, particularly digital twins, to enhance productivity and sustainability. Digital twins create real-time virtual models of agricultural systems, allowing for improved decision-making in crop growth, soil health, and resource use by integrating IoT sensors, AI, machine learning, satellite imagery, and cloud analytics. Studies show that these technologies can boost crop yields by 10-25%, reduce water usage by 20-30%, and cut fertilizer application by 15-20% while preserving soil quality. The proposed architecture for agricultural digital twins includes data acquisition, modeling, predictive analytics, and decision support, with applications in crop simulation, soil monitoring, and precision irrigation. Nevertheless, challenges like high infrastructure costs and data interoperability hinder broader implementation. Ultimately, digital twins represent a transformative shift toward intelligent, sustainable agricultural practices.
- 7 May 2026
- Published by IGI Global Scientific Publishing
- p. 249-286
Digital Twins for Smart Crop, Soil, and Resource Optimization in Modern Agriculture
Modern agriculture is increasingly leveraging digital technologies, particularly digital twins, to enhance productivity and sustainability. Digital twins create real-time virtual models of agricultural systems, allowing for improved decision-making in crop growth, soil health, and resource use by integrating IoT sensors, AI, machine learning, satellite imagery, and cloud analytics. Studies show that these technologies can boost crop yields by 10-25%, reduce water usage by 20-30%, and cut fertilizer application by 15-20% while preserving soil quality. The proposed architecture for agricultural digital twins includes data acquisition, modeling, predictive analytics, and decision support, with applications in crop simulation, soil monitoring, and precision irrigation. Nevertheless, challenges like high infrastructure costs and data interoperability hinder broader implementation. Ultimately, digital twins represent a transformative shift toward intelligent, sustainable agricultural practices.