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Agroeconomic: A Smart Dynamic Pricing System to Empower Local Farmers Using Market Intelligence


Authors : Keerthana S.; Jennifer Mary S.; Dr. Girish Kumar D.

Volume/Issue : Volume 11 - 2026, Issue 5 - May


Google Scholar : https://tinyurl.com/57463buh

Scribd : https://tinyurl.com/mrbbwssx

DOI : https://doi.org/10.38124/ijisrt/26May495

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : Recent advances in data analytics, cloud-based web technologies, and intelligent decision-support systems have enabled the development of digital platforms that address inefficiencies in agricultural markets. This work presents Agroeconomic, a smart dynamic pricing and market intelligence system designed to support local farmers by providing realtime price insights, product visibility, and decision guidance. The proposed framework integrates market data processing, user authentication, crop listing management, and a recommendation-driven pricing module to estimate fair crop prices under varying market conditions. A modular web interface developed using modern frontend technologies enables seamless interaction across multiple functional modules, including dynamic pricing visualization, multilingual voice translation, interactive chat support, and analytical insights. Secure checkout and digital payment modules facilitate transparent and efficient transactions. The system architecture is scalable and deployable on cloud environments, ensuring accessibility for geographically distributed users. Experimental evaluation demonstrates improved price awareness, reduced information asymmetry, and enhanced farmer engagement with digital marketplaces. The study emphasizes the role of intelligent pricing systems and integrated market intelligence in promoting equitable trade, economic sustainability, and digital empowerment within the agricultural sector.

Keywords : Smart Agriculture, Dynamic Pricing System, Market Intelligence, Agricultural Decision Support, Crop Price Recommendation, Farmer Empowerment, Web-Based Agricultural Platform, Multilingual Voice Assistance, Digital Marketplace, Secure Online Transactions, Data-Driven Agriculture.

References :

  1. R. Mehta, S. Kulkarni, and P. Rao, “Artificial intelligence applications in smart agriculture: A comprehensive review,” IEEE Access, vol. 9, pp. 124567–124582, 2021.
  2. A. Banerjee and N. Malhotra, “Machine learning techniques for agricultural price prediction and market analysis,” Journal of Agricultural Informatics, vol. 12, no. 3, pp. 45–58, 2021.
  3. K. Reddy and S. Patil, “Data-driven decision support systems for farmers using market intelligence,” International Journal of Agricultural Economics, vol. 6, no. 2, pp. 89–101, 2020.
  4. M. Das, R. Chatterjee, and A. Ghosh, “Dynamic crop pricing models using artificial intelligence and demand–supply analysis,” Computers and Electronics in Agriculture, vol. 181, Article ID 105945, 2021.
  5. P. Verma and S. Jain, “Web-based intelligent pricing systems for agricultural supply chains,” International Journal of Information Systems in Agriculture, vol. 9, no. 1, pp. 12–25, 2020.
  6. H. Liu, Y. Zhang, and L. Wang, “Random forest–based prediction of agricultural commodity prices,” Journal of Big Data Analytics in Agriculture, vol. 4, pp. 67–80, 2021.
  7. S. Mukherjee and T. Sen, “Market intelligence platforms for smallholder farmers: Opportunities and challenges,” IEEE Technology and Society Magazine, vol. 40, no. 4, pp. 34–42, 2021.
  8. A. Kumar and R. Singh, “Cloud-enabled smart agriculture systems for real-time price analytics,” International Journal of Cloud Computing and Services Science, vol. 10, no. 2, pp. 95–108, 2021.
  9. N. Patel, J. Shah, and V. Meena, “Integration of machine learning and economic indicators for crop price forecasting,” IEEE Access, vol. 9, pp. 98721–98734, 2021.
  10. L. Fernandes and M. Costa, “Design and evaluation of intelligent decision-support systems for agricultural markets,” Journal of Intelligent Systems in Agriculture, vol. 5, no. 2, pp. 140–154, 2022.

Recent advances in data analytics, cloud-based web technologies, and intelligent decision-support systems have enabled the development of digital platforms that address inefficiencies in agricultural markets. This work presents Agroeconomic, a smart dynamic pricing and market intelligence system designed to support local farmers by providing realtime price insights, product visibility, and decision guidance. The proposed framework integrates market data processing, user authentication, crop listing management, and a recommendation-driven pricing module to estimate fair crop prices under varying market conditions. A modular web interface developed using modern frontend technologies enables seamless interaction across multiple functional modules, including dynamic pricing visualization, multilingual voice translation, interactive chat support, and analytical insights. Secure checkout and digital payment modules facilitate transparent and efficient transactions. The system architecture is scalable and deployable on cloud environments, ensuring accessibility for geographically distributed users. Experimental evaluation demonstrates improved price awareness, reduced information asymmetry, and enhanced farmer engagement with digital marketplaces. The study emphasizes the role of intelligent pricing systems and integrated market intelligence in promoting equitable trade, economic sustainability, and digital empowerment within the agricultural sector.

Keywords : Smart Agriculture, Dynamic Pricing System, Market Intelligence, Agricultural Decision Support, Crop Price Recommendation, Farmer Empowerment, Web-Based Agricultural Platform, Multilingual Voice Assistance, Digital Marketplace, Secure Online Transactions, Data-Driven Agriculture.

Paper Submission Last Date
30 - June - 2026

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