ESTD Year: 2017 | Impact Factor: 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 9 (2026): Volume 10, Issue 9, September 2026 | Pages: 130-137
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 26-09-2026
Agriculture industry ranks amongst the most important industries which play a very critical role in economic development and livelihood generation. However, there are several challenges faced in agriculture making it hard to conduct business such as lack of price information, dependence on middlemen and inefficiencies in decision-making tool used by the farmers. In this paper, AgriLink web-based agricultural application is proposed. This will assist farmers in making effective decisions in the market. The framework provided in the paper allows farmers to make comparisons regarding crop prices, estimating profits and direct communication with the vendors. The framework makes use of inputs from the users, market price information and the process of decision making to generate an optimal solution for selling of crops. Matching algorithm matches farmers with relevant vendors based on crop types and location, and transportation costs are estimated to compute profit. Application framework for the web-based system is adopted whereby the user enters crop details and gets instant feedback. In addition to making the markets transparent, the proposed platform can be employed in ensuring that the process of agricultural digitization takes place because it can facilitate quick communication among the stakeholders without delaying decision making. The platform also has a lightweight and scalable system architecture, thus being suitable for implementation in rural areas owing to low computing power. In view of the integration of market intelligence and profitability analysis tools, the platform becomes very useful for smallholder farmers.
Smart Agriculture, Decision Support System, Market Analysis, Profit Forecasting, Web Platform
Thota Mallika Devi, K.Sreeveda, Tejaswi Sai Bhimavarapu, R Gayathri, Chitrala Nikitha, & M Likitha. (2026). AgriLink: Smart Agricultural Market Platform Using Data-Driven Decision Support. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(9), 130-137. Article DOI https://doi.org/10.47001/IRJIET/2026.109014
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
Nandakumar, C., Anisha, T., Harshavardhan, B., Divya, P.,& Sai Krishna, S. (2023). EcoFresh – An integrated farmer market platform with AI-powered NLP chatbot to strengthen agricultural trade and simplify e-commerce operations. International Journal of Advanced Research in Science, Communication and Technology, 8(2).
S. R. Nandurkar, V. R. Thool, and R. C. Thool, “Design and Development of Precision Agriculture System Using Wireless Sensor Network”, International Journal of Advanced Computer Science and Applications, vol. 5, no. 6, 2014.
Mittal, S., & Mehar, M. (2016). Socio-economic factors affecting adoption of modern information and communication technology by farmers in India. Indian Journal of Agricultural Economics.
Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture.
K. Nirmal Ravi Kumar, “Minimum support prices and market price dynamics in Indian agriculture”, Discover Sustainability, vol. 7, article 1305, 2026. This paper studies relationships between minimum support prices, production costs, domestic market prices, and international prices in Indian agriculture.
P. Kumar, R. R. Kumar, and G. K. Jha, “Agricultural price forecasting with multivariate singular spectrum analysis across multiple Indian markets,” Discover Artificial Intelligence, vol. 6, article 980, 2026. This research concentrates on the forecasting of agricultural prices across multiple Indian markets.
P. Mahajan, A. A. Muley, and M. R. Fegade, “Forecasting agricultural commodity prices using machine learning models,” Discover Informatics, vol. 1, article 15, 2026. The research utilizes historical AGMARKNET data from Maharashtra and uses machine-learning-based approach for agricultural commodities.
K. Dipu, N. Tyagi, and R. Khan, “Explainable AI-Based Crop Price Prediction Model for Robust Stakeholder Decision-Making,” 2026 IEEE 6th International Conference on Computing, Power, and Communication Technologies (IC2PCT), 2026. DOI: 10.1109/IC2PCT68894.2026.11584437. The paper focuses on explainable AI for crop-price prediction and presents forecasts through a decision-support dashboard.
M. Shamsul Arefin, M. I. S. Mahin, F. A. Mily, M. S. H. Sani, M. I. Rehan, and T. I. Sumon, “AGRO AI: A compact solution for modernizing the agriculture using NASA’s satellite data and artificial intelligence,” Applied Food Research, vol. 6, article 101678, 2026. The system combines satellite data, IoT data, AI, and an agricultural chatbot for real-time recommendations.
“AIoTU: An open-source IoT and machine learning platform for smart agriculture and yield prediction,” SoftwareX, vol. 34, article 102613, 2026. The platform combines IoT, weather information, machine learning, real-time monitoring, and yield prediction for smart agriculture.