FarmConnect: Smart Agricultural Marketplace Using AI and MERN Stack

Abstract

Agriculture is the backbone of the Indian economy, yetfarmersfacesignificantchallengesinmarketingtheirproduce and receiving fair compensation. The presence of intermediaries often reduces farmers’ profits and limits direct access to con-sumers. To address this issue, this paper proposes FarmConnect, a web-based e-commerce platform that enables farmers to sell their products directly to consumers without the involvement of middlemen. The platform provides a virtual marketplace where farmers can create profiles, list products, set prices, and manage sales independently. Consumers can easily browse, search, and pur-chase fresh agricultural produce at affordable prices through a user-friendlyinterface.ThesystemisdevelopedusingtheMERN stack,ensuringscalability,dynamicinteraction,andefficientdata management through MongoDB. Additionally, FarmConnect includes separate login modules for farmers and consumers, secure payment functionality, and a guidelines module that educates farmers about crop cultivation, soil types, and best practices. The proposed solution aims to improve transparency, enhance farmer income, and promote a sustainable and technology-driven agricultural ecosystem.

Introduction

Agriculture is the backbone of the Indian economy, but many farmers earn low incomes due to dependence on intermediaries, limited access to real-time market prices, and inadequate market connectivity. Existing digital agriculture solutions mainly focus on weather forecasting, crop monitoring, storage, or communication, but fail to provide a comprehensive platform that directly connects farmers with consumers. Although technologies such as Machine Learning (ML), Cloud Computing, and Data Analytics have improved agricultural decision-making, an integrated, secure, and user-friendly marketplace remains unavailable.

To address these challenges, the proposed FarmConnect platform offers a unified digital marketplace that enables direct farmer-to-consumer transactions while eliminating middlemen. The platform integrates AI-powered crop recommendation, fertilizer prediction, plant disease detection, weather-based pest prediction, secure authentication, geolocation-based search, transparent ratings, and UPI-enabled payments. Built using React, Node.js, Express.js, MongoDB, Python Flask, and OpenWeatherMap APIs, the system follows a modular, scalable architecture consisting of frontend, backend, database, ML server, and external API layers.

The literature review highlights that previous research has addressed individual agricultural problems such as weather forecasting, IoT-based storage, online marketplaces, blockchain security, and AI-based crop analysis, but none provides a complete end-to-end agricultural marketing ecosystem. FarmConnect fills this gap by combining e-commerce, AI-based advisory services, secure transactions, and real-time agricultural information within a single platform.

The system includes six major modules: Crop Recommendation using ensemble ML models, Fertilizer Recommendation using CatBoost, Plant Disease Detection using MobileNetV2, Weather-based Pest Prediction, Marketplace and Order Management, and Authentication with JWT-based security and role-based access control. These modules work together to improve productivity, reduce crop losses, and simplify buying and selling for both farmers and consumers.

FarmConnect is evaluated based on pricing effectiveness, matchmaking performance, scalability, operational efficiency, database performance, and security. The expected outcomes include higher farmer income, transparent pricing, improved market access, better decision-making through AI, secure digital transactions, and enhanced agricultural productivity. Experimental evaluations indicate that the proposed AI models outperform existing approaches in crop recommendation, fertilizer prediction, and plant disease detection, demonstrating higher accuracy, precision, recall, and F1-scores.

Despite its advantages, the platform has some limitations. AI prediction accuracy depends on the quality and diversity of training data, rural internet connectivity may restrict accessibility, and scalability challenges may arise with large user populations. Nevertheless, FarmConnect provides a strong foundation for a modern, technology-driven agricultural ecosystem that can be further enhanced through real-world deployment and continuous improvements.

Conclusion

The FarmConnect platform provides an efficient and pio-neering approach to addressing the problems of conventional agricultural supply chains through the provision of direct en-gagement between key stakeholders, specifically farmers and consumers. The removal of intermediaries allows farmers to earn reasonable profits from their products, while also provid-ingconsumerswithgreateraccesstofreshandaffordablefood. By combining a digital marketplace with UPI-based payment methods,thesystemenhancestransactiontransparency,speed, and user trust. Furthermore, the platform supports small and marginal farmers by offering improved market access through digital channels, reducing reliance on traditional distribution systems. In addition, the integration of advanced technologies suchasmachinelearning(ML)anddeeplearning(DL)further strengthens the capabilities of FarmConnect. Features includ-ing crop recommendation, fertilizer prediction, plant disease detection, and weather-based pest alerts assist farmers in making informed decisions, thereby improving productivity and reducing crop losses. Modern web technologies ensure scalability,security,andefficientsystemperformance.Asa result, FarmConnect contributes to the development of a transparent,intelligent,andsustainableagriculturalecosystem, withstrongpotentialforreal-worldimplementationandfuture enhancements.

References

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Copyright

Copyright © 2026 Sumeet Nikam, Kaustubh Thorat, Milind Patil, Chirag Fegade, Dr. Kalyan D. Bamane, Mrs. Sonal Mohite . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.