AI-Powered Price Prediction for Agriculture Markets
DOI:
https://doi.org/10.15662/IJEETR.2026.0802005Keywords:
`Artificial Intelligence, Machine Learning, Price Forecasting, Agriculture, Market Prediction, Data AnalyticsAbstract
Farmers, traders and policymakers face uncertainty due to fluctuation in the price of agricultural products and it has normally resulted in financial losses and inferior market decisions. The conventional approaches of forecasting crop prices use past trends and manual forecasting, which are not accurate and adaptable to changing market conditions.
The current paper will suggest an AI-based Agricultural Price Prediction System based on the application of Machine Learning, past market data, weather conditions, demand and supply analysis, and seasonal changes to predict the prices of crops. The system gives the farmers predictions of their prices in advance so that they may make better decisions in selling their produce, they will not depend on middlemen and their income will be more stable. The given model will promote the transparency of agricultural markets and make evidence-based decisions.
References
1. Cheng et al. (2025) proposed an SVM-based agricultural crop price prediction model.
2. Manogna et al. (2025) evaluated the performance of deep learning models, including LSTM and GRU, for agricultural commodity price forecasting.
3. Dutt et al. (2024) developed an artificial intelligence-based model for agricultural price prediction.
