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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67648
Volume:
10 |
10 |
Oct 2026
Pages:
1--
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science and Engineering
Rajiv Gandhi Institute of Technology
Department of Computer Science and Engineering
Rajiv Gandhi Institute of Technology
Department of Computer Science and Engineering
Rajiv Gandhi Institute of Technology
Department of Computer Science and Engineering
Rajiv Gandhi Institute of Technology
Department of Computer Science and Engineering
Rajiv Gandhi Institute of Technology
- Rural communities face challenges in agriculture, resource management and getting timely information because of changing weather, few resources and not enough digital help. Artificial Intelligence can help solve these challenges by looking at farm and environmental data and giving useful advice. The plan is to build an Artificial Intelligence framework that supports innovation and sustainable growth by using government and other organized data sets. This framework helps with farm tasks such as predicting crop yield choosing crops managing resources deciding on pest and disease actions and connecting to markets. Artificial Intelligence uses machine learning and deep learning to find patterns, in the data. These patterns help produce predictions. The predictio ns help farmers make choices. The plan also looks at using resources to cut down extra use of water, fertilizers and other farm supplies. A simple interface is made so the information is easy to read and apply. By mixing data-based predictions, with decision help the Artificial Intelligence framework aims to raise farm output use resources more efficiently and help rural communities grow sustainably.
KEYWORDS
The proposed AI-driven framework for innovation and sustainable systems brings together many different kinds of agricultural and environmental data. These data include IoT-sensed information, weather reports, remote-sensing images and old agricultural records. By processing and combining these data applying AI and machine-learning analysis the AI-driven framework for rural innovation and sustainable systems is built to help people make smarter farming choices and manage resources in a sustainable way. The AI-driven framework for innovation and sustainable systems is not meant to look at just one job or one data source. Instead it offers a unified method for studying many farming factors at once. By using data-driven analysis and giving easy-to-use decision support the AI-driven framework for innovation and sustainable systems helps rural people watch how their farms are doing spot useful patterns and decide what to do next. Future work will concentrate on putting the framework for rural innovation and sustainable systems into practice with bigger real-world data sets. We will also improve how well the models work by using AI techniques and we will make the system easier to grow and easier for people to use. Adding real-time IoT data and fresh remote-sensing information will also make the framework for rural innovation and sustainable systems even more useful, for real sustainable rural growth. label, present them within parentheses. Do not label axes not label axes with a ratio of quantities and units. For example, write “Temperature (K)”, not “Temperature/K”.
[1] A. I. Olutumise, "A review of the economic impact of artificial intelligence on agricultural productivity, sustainability and rural livelihoods," Discover Agriculture, 2026. [2] A. Rajbongshi et al., "Leveraging AI for sustainable agriculture: A comprehensive review of recent advances," Artificial Intelligence Review, 2026. [3] A. K. M. K. Pervez et al., "Mapping recent trends in artificial intelligence research for sustainable agriculture: A bibliometric and systematic review," Discover Artificial Intelligence, 2026. [4] S. Sukriadi, A. Adawiah, and I. Ismail, "Leveraging machine learning for sustainable smart farming in rural landscapes," Discover Food, 2026. [5] S. Satpati, "Systematic review of artificial intelligence in precision agriculture for smallholder farmers," Discover Global Society, 2026. [6] Z. Ali et al., "Artificial intelligence for sustainable agriculture: A comprehensive review of AI-driven technologies in crop production," Sustainability, 2025. [7] P. Wu and Y. Zhong, "Artificial intelligence in sustainable agriculture: Towards a socio-technical roadmap," Smart Agricultural Technology, 2025. [8] I. A. Bhat et al., "Leveraging artificial intelligence in agribusiness: A structured review of strategic management practices and future prospects," Discover Sustainability, 2025. [9] A. O. Omotayo et al., "Artificial intelligence in agriculture: Ethics, impact possibilities, and pathways for policy," Computers and Electronics in Agriculture, 2025. [10] V. Anand et al., "Systematic review of machine learning applications in sustainable agriculture: Insights on soil health and crop improvement," Phyton-International Journal of Experimental Botany, 2025.
Publication Date
10/3/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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