Environmental Computing and ICT-Driven Agricultural Engineering: Sustainable Solutions for Modern Agriculture
Innovative methods in agriculture are required to address the persistent problems of resource scarcity, climate change, and food security. This study investigates how information and communication technology (ICT) and environmental computing contribute to the development of sustainable agricultural engineering solutions. The study explores how advanced data analytics, IoT devices, and AI-driven apps may optimise resource utilisation, boost production, and foster environmental stewardship in agricultural systems. Precision agriculture, in which agricultural techniques are informed by real-time data from linked sensors, and smart irrigation systems, which drastically minimise water usage while optimising crop yields, are two important areas of concentration. The study also looks at how autonomous equipment and drones may be used to monitor crop health and improve operational efficiency. According to the research, using ICT and environmental computing promotes a more sustainable agricultural ecosystem by reducing adverse environmental effects in addition to increasing agricultural productivity. This effort intends to give actionable insights and frameworks for attaining sustainable agriculture, assuring the resilience and adaptation of food production systems in the face of changing global problems. This will be accomplished through interdisciplinary collaboration and the deployment of novel technology.
Authors
- M. Harikrishna
- Anurag Shrivastava (ORCID: https://orcid.org/0000-0002-8189-475X)
- Gaurav Pushkarna
- Kanchan Yadav (ORCID: https://orcid.org/0000-0003-2403-1998)
- Himanshu Rai Goyal (ORCID: https://orcid.org/0000-0002-7277-5644)
- Mohammed I. Habelalmateen
Institutions
- Lovely Professional University (IN)
- Iraqi University (IQ)
- Graphic Era University (IN)
- GLA University (IN)
- Saveetha University (IN)
Publication Details
- Published
- 2024-12-13
- DOI
- https://doi.org/10.1109/ictbig64922.2024.10911773
- Citations
- 1
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.53