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Dr. P. Ravinder Rao, Neerati Saiprakash, Bijja Saikrishna, Gone Akshay
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Abstract : Agricultural commodity price forecasting is decisive for farmers, traders, representatives, and stakeholders to make knowledgeable decisions. This project leverages AI and Machine Learning (ML) techniques to develop a predictive analytics system for forecasting the prices of agricultural commodities. We train ML models by using historic price data, weather conditions, market trends, and other relevant factors. The system uses Python, with Scikit-learn for model development and Streamlit for an interactive user interface. The trained ML model is serialized using Pickle, allowing for efficient storage and deployment. Various regression techniques, counting Linear Regression, Random Forest, and Support Vector Machines (SVM), are explored to enhance forecasting accuracy. This application enables users to input relevant parameters and receive price predictions dynamically. This AI-ML-driven approach enhances decision-making capabilities, reduces risks, and improves financial planning in the agricultural sector.
Keyword Machine Learning, Predictive Analytics, Agricultural Commodities, Price Forecasting, Python, Streamlit, Scikit-learn, Pickle.
Reference:
  1. Sandhu Dutt, Prateek N Kulkarni, Shilpa Akilan M, Rishav Mishra, Pratik Khetan and Animesh Bhadora;” AGRICULTURAL PRICE PREDICTION THROUGH ARTIFICIAL INTELLIGENCE ”; International Journal of Development Research, Vol. 14, Issue, 03, pp. 65161-65165, March 2024.
  2. Zhiyuan Chen*1, Howe Seng Goh1, Kai Ling Sin1, Kelly Lim1, Nicole Ka Hei Chung1, Xin Yu Liew1 ,” Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques” , Advances in Science, Technology and Engineering Systems Journal, 6, No. 2, 2021.
  3. Y. Chen, K.L.Sin, “Long Short-Term Memory Model Based Agriculture Commodity Price Prediction Application,” in Proceedings of the 2020 2nd International Conference on Information Technology and Computer Communications, Association for Computing Machinery, New York, NY, USA: 43–49, 2020, doi:10.1145/3417473.3417481.
  4. Zulauf, C. R., Irwin, S. H., Ropp, J. E. and Sberna, A. J. 1999. A reappraisal of the forecasting performance of corn and soybean new crop futures. Journal of Futures Markets, 19(5), 603–618.
  5. Ravinder Rao and V. Sucharita, “A Secure Cloud Service Deployment Framework for DevOps,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 2, pp. 874-885, February 2021.
  6. Ravinder Rao and V. Sucharita, “A Framework to Automate Cloud-based Service Attacks Detection and Prevention,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 2, pp. 241, 2019.

Abstract

Forecasting agricultural commodity prices is vital for enabling informed decision-making by farmers, traders, and other stakeholders. This project introduces an AI- and Machine Learning (ML)-based predictive analytics system designed to accurately forecast the prices of key agricultural commodities. The system leverages historical price trends, climatic conditions, and market dynamics to train machine learning models capable of delivering reliable predictions.

Developed in Python, the platform utilizes Scikit-learn for model building and Streamlit for a user-friendly interactive interface. The machine learning models are serialized with Pickle to ensure fast and efficient deployment. The study evaluates multiple regression models—including Linear Regression, Random Forest, and Support Vector Machines (SVM)—to identify the most accurate approach for commodity price prediction.

This intelligent system allows users to input real-time variables and obtain dynamic price forecasts, supporting improved planning, reduced financial risks, and optimized decision-making within the agricultural ecosystem.

Introduction

Agriculture remains a cornerstone of the global economy, contributing significantly to food security and rural livelihoods. However, the sector faces persistent challenges due to price volatility in essential commodities like onions, pulses, and potatoes. These unpredictable fluctuations create uncertainty for producers, market inefficiencies for traders, and planning difficulties for policymakers.

Accurate agricultural price forecasting helps farmers and traders make smarter decisions. This project uses AI and machine learning to predict prices of commodities like onions and pulses. It combines historical price data, weather reports, and market trends. Tools like Python, Scikit-learn, and Streamlit power the system. The model is saved using Pickle for faster access. Techniques such as Linear Regression, SVM, and Random Forest boost accuracy. As a result, users get real-time price updates through a simple interface. Moreover, the platform reduces risks, improves financial planning, and supports smarter farming—making it a powerful solution for the agriculture sector.Agricultural Price Forecasting

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