FARMINTEL – CROP COLLECTION AND MARKET INTELLIGENCE PLATFORM

FarmIntel is a smart agricultural market intelligence platform that combines agricultural operations with machine learning and predictive analytics to support farmers and other stakeholders. The system integrates crop collection management, price forecasting, demand prediction, inventory tracking, and compensation management into a single platform. This helps improve decision-making, increase operational efficiency, and provide better market opportunities for farmers.   The platform enhances market transparency by providing accurate market insights and reducing uncertainty in crop pricing and demand. It helps minimize post-harvest losses, improves communication between farmers and buyers, and contributes to higher profitability. The centralized management system also enables efficient monitoring of agricultural activities and transactions.   In the future, FarmIntel can be enhanced by developing a mobile application for easier access to services and notifications. Integration with IoT devices can enable real-time crop and environmental monitoring. Advanced AI techniques can be used to provide personalized farming recommendations, crop disease detection, and yield prediction. Additionally, connecting the platform with government agricultural schemes and weather forecasting services can further benefit farmers and improve agricultural productivity.   Overall, FarmIntel demonstrates how modern technologies can be used to create a more efficient, transparent, and data-driven agricultural ecosystem, supporting sustainable growth in the farming sector.

[1]              Bhende, M., Avatade, S., et al. "Digital Market: E-Commerce Application For Farmers," Proceedings of the IEEE International Conference on Computing Methodologies and Communication (ICCMC), 2019, pp. 452–457. [2]              Kruize, J., Robbemond, R., et al. "A Reference Architecture for Farm Software Ecosystems," Computers and Electronics in Agriculture (Elsevier), vol. 128, 2016, pp. 12–28. [3]              Sørensen, C. G., Fountas, S., et al. "Reference Architecture Design for Farm Management Information Systems: A Multi- Case Study Approach," Precision Agriculture (Springer), vol. 22, no. 1, 2021, pp. 22–50. [4]              Saranya, G., & Nagarajan, M. "Design Framework for Web-Based Agronomic Systems and Decoupled Microservice Implementations," International Journal of Computer Applications, vol. 174, no. 12, 2020, pp. 31–36. [5]              Gunasekaran, M., Thapaswini, G., et al. "A Methodology for Crop Price Prediction Using Machine Learning," Proceedings of the IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC), 2022, pp. 188–193. [6]              Mohanty, A., & Panda, S. "An Intelligent Decision Support Architecture for Market Price Forecasting and Supply Determination," IEEE Access, vol. 12, 2024, pp. 106690–106701. [7]              Bhat, S., & Ramesh, K. "AI-Based Smart Crop Recommendation and Price Prediction System Using Enterprise Regressors," ResearchGate Open Archive, 2026. [8]              Ashwitha, A., & Latha, C. A. "Crop Recommendation and Yield Estimation Using Machine Learning Ensemble Models," IEEE Transactions on Sustainable Agricultural Technologies, vol. 3, no. 2, 2024, pp. 862–870. [9]              Kavitha, J., & Mathur, P. "A Hybrid Machine Learning Framework for Optimizing Crop Distribution Channels," arXiv Computer Science Repository, 2025, arXiv:2507.08832. [10]           Kumar, R., & Naidu, S. "AI Based Smart Crop Prediction System for South Indian Agrarian Corridors," Journal of Agricultural Data Science, vol. 14, no. 3, 2026, pp. 112–121.