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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63661
Volume:
10 |
5 |
May 2026
Pages:
1-5
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.
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY,DAVANGERE
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY,DAVANGERE
MASTER OF COMPUTER APPLICATIONS
BAPUJI INSTITUTE OF ENGINEERING AND TECHNOLOGY,DAVANGERE
Generative AI for Paddy Stream Restraint with Profit Analysis is presented as a digital platform to support transparency, efficiency and data-driven decision making across agricultural operations, and integrates data from multiple stages, including cultivation, transportation, milling and retail and applies analytical and generative artificial intelligence techniques to produce actionable insights. Interactive dashboards and visualizations are used to present cost, profit, and performance trends in an accessible and interpretable manner. These visual tool support evaluation of supply chain behaviour, identification of inefficiencies and assessment of optimization opportunities. Information gaps between farmers, transporters, mill owners, and retailers are reduced through shared access to consistent and reliable data. Improved transparency supports fair pricing, stronger coordination and increased trust among participant’s chain. The modular and scalable architecture supports future expansion to additional crops, regional variations, mobile access and advanced predictive analytics. This platform demonstrates how generative artificial intelligence and digital technologies can strengthen agricultural supply chains, improve farmer empowerment and promote sustainable agricultural practices. User interaction focuses on clarity easy to use and fast feedback. Input forms support structured data entry and reduce manual errors. Visual elements guide attention toward key cost patterns and performance indicators. This design supports consistent use across different user groups and operational contexts.
KEYWORDS
The "Generative AI for Paddy Stream Restraint with Profit Analysis" demonstrates a digital platform for analysis of the paddy supply network. Data from cultivation transportation milling and retail stages enters a single system. Cost values revenue figures and profit margins appear through structured dashboards and visual components. Web technologies and Artificial Intelligence convert raw inputs into meaningful insights. Charts summaries and generated explanations support understanding of supply chain performance. Inefficiencies delays and cost leakages appear through data patterns. Decision making shifts from assumption-based thinking toward evidence-based evaluation. Modern software supports agricultural processes through data handling visualization and insight generation. Planning accuracy improves risk reduces. Transparency increases across participating roles.
[1]. J. Smith R. Kumar and A. Patel Artificial Intelligence Applications in Smart Agriculture Springer 2021. [2]. P. R. Deshpande and S. K. Verma “AI-Based Irrigation Management Systems for Sustainable Farming” International Journal of Agricultural Technology vol. 15 no. [3] pp. 245–252 2020. 3. M. Brown and L. Zhang “Generative Artificial Intelligence for Resource Optimization in Agriculture” IEEE Access vol. 9 pp. 112345–112356 2021. [4]. Food and Agriculture Organization (FAO) Water Management in Paddy Cultivation FAO Publications Rome 2019. [5]. A. Chandra and N. Rao “Profit Analysis and Decision Support Systems for Precision Farming” Journal of Smart Farming Technologies vol. 7 no. 2 pp. 89–96 2022. [6]. Chopra, S., & Meindl, P. (2019). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson Education. [7]. Food and Agriculture Organization of the United Nations. (2018). Rice Market Monitor and Analysis of Global Rice Supply Chains. FAO Publications. [8]. Lambert, D. M., & Cooper, M. C. (2000). Issues in Supply Chain Management. Industrial Marketing Management, 29(1), 65–83. [9]. K. T. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, “Machine Learning in Agriculture: A Review,” Sensors, vol. 18, no. 8, pp. 1–29, 2018. [10]. R. Wolfert, L. Ge, C. Verdouw, and M. Bogaardt, “Big Data in Smart Farming – A Review,” Agricultural Systems, vol. 153, pp. 69–80, 2017.
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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