EventsThe 5th International Electronic Conference on Applied Sciences
Published
This submission belongs to the session S3. Computing and Artificial Intelligence of the event The 5th International Electronic Conference on Applied Sciences
Published date
02 Dec, 2024
Academic Editor
author-avatarEugenio Vocaturo
Citation
Ankit Krishna, Neelamadhab Padhy, rasmita Panigrahi, Archana Patnaik, Farm Forecast: An AI-Powered predictive system for stabilizing Agricultural council prices., in Proceedings of The 5th International Electronic Conference on Applied Sciences, 4 December–6 December 2024, MDPI: Basel, Switzerland
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Farm Forecast: An AI-Powered predictive system for stabilizing Agricultural council prices.

1. school of engineering and technolgy,department of computer science and engineering ,GIET university,gunupur, odisha, India
Abstract

The high price volatility of agri-horticultural commodities, especially essential ones like pulses, onions, and potatoes, poses formidable economic challenges to consumers, producers, and government bodies. The Department of Consumer Affairs is under constant pressure to stabilize markets, address inflation, and support sustainable agriculture. Farm Forecast solves this by providing a predictive analytics platform that combines artificial intelligence with cutting-edge algorithms to predict the future price movement of major agricultural commodities.

The Aurelius Market Intelligence System utilizes the ARIMA (Autoregressive Integrated Moving Average) model to examine past data and factors like weather, political affairs, supply chain bottlenecks, and trade scenarios globally. Farm Forecast is able to deliver an out-of-the-box forecast, stretching up to a full year ahead, on expected prices for maize, thus empowering different stakeholders like farmers, policy makers, and retailers to take informed decisions pertaining to production schedules, buffer stock management, pricing strategies, etc.

This user-friendly web dashboard offers interactive real-time insights and predictive analytics. This provides a chance for farmers to select crops as per the market demand, while government agencies can act before they are outsold due to sudden price changes. It also allows for automatic alerts to the Department of Consumer Affairs when market prices anticipate a shock (and hence increase uncertainty and price volatility).

In the future, Farm Forecast will be upgraded to include real-time data streams and operate in other crop markets. We plan to experiment with more advanced models like neural networks as the system matures to enhance prediction quality even further. Farm Forecast combines this mash-up of machine learning and economic modelling to provide a transformative tool for stabilizing commodity prices that benefits consumers as much as it does the agriculture industry.

Keywords
Agricultural commodities
Price volatility
Prediction analytics
ARIMA model
Machine learning
Economic stability Buffer stock management
Real time Data Integration
Governmentinterventions
AI driven solutions Market forecasting.
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