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International Journal of Computer Techniques

ISSN 2394-2231

DOI Registered

Volume 12, Issue 6 | Published: November – December 2025

Author

Rudrani Girish Jangale , Riya Antha , Srushti Bhaskar Khatale , Srushti Bajirao Kshirsagar

Abstract

Crop price prediction plays a vital role in today’s agriculture, impacting everything fromfarmer profits to market stability and even policy decisions. With the increasing complexity of agricultural systems—thanks to unpredictable weather, varying soil types, regional demands, and global trade there’sa real need for sophisticated computational models to make accurate forecasts. Harvest Horizonintro- duces a data-driven framework that leverages machine learning (ML) and deep learning (DL) techniques, all built on a scalable PySpark-based preprocessing pipeline. The project employs a variety of models, including Convolutional Neural Networks (CNN),Long Short-Term Memory (LSTM) networks,Random Forest (RF), and XGBoost, to delve into the temporal, spatial, and nonlinear relationships foundin agricultural data. By tapping into historical price data, climate factors, and regional specifics, the system can predict crop prices tailored to specific areas and display the findings through an interactive dashboard. The experimental results show that hybrid deep learning models surpass traditional machine learning methods in terms of both accuracy and flexibility. This study underscores the promise of artificial intelligence in agricultural analytics, offering valuable insights for farmers, traders, and policymakers alike.

Keywords

Keywords:Agricultural Price Fore- casting , Data Analytics in Agriculture, Ma- chine Learning Prediction Models,Time Series Forecasting,Crop Market Value Prediction, Data- Driven Farming Decisions,Azure Machine Learning Studio, Big Data in Agriculture,Market Trend Analysis,Regression Analysis, Predictive Analytics, Agriculture Data Modeling,Farmer Decision Support System,Data Preprocessing and Cleaning,Data Visualization for Agriculture Markets,Commodity Price Analysis, Historical Crop Price Data,Agricultural Data Forecasting Frame- work.

Conclusion

In conclusion, the Harvest Horizon – Data Driven Decision in Farming amp; Market Pricing project is all about helping farmers make smarter choices about when and where to sell their crops by predicting future prices using old market data. It uses past APMC market data to train prediction models on Microsoft Azure, giving pretty good market value guesses without needing weather or soil info. By cleaning, analyzing, and using machine learning on the data, the project shows how forecasting can ease some of the uncertainty farmers face. They can pick the best time and place to sell their goods to get the most money. Using cloud tech keeps the data safe and makes sure the system can grow and train models without problems, even with more data. Right now, Phase 1 is about building the dataset, training the model, and checking how right the preditions are. Phase 2 could add a visual dashboard and make the model even better. Overall, Harvest Horizon is a cool and useful way to make farmers more aware of the market using data to guess what will happen. It gives farmers and others involved a look into future market trends, supporting hon- est trading, earnings, and data-smart decisions in farming.

References

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How to Cite This Paper

Rudrani Girish Jangale , Riya Antha , Srushti Bhaskar Khatale , Srushti Bajirao Kshirsagar (2025). Harvest Horizon: Data Driven Decisions in Farming Market Pricing. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.