Farm Mart: Price Prediction Using Machine Learning

Farm Mart: Price Prediction Using Machine Learning

{BE Students}* Patil Aniket Krishna, Pawase Nilima Babasaheb, Salunke Shrutika Nilkanth, Vatharkar Pragati Rahul

{Faculty}** Tanuja Shrikant Dhage, Sachin Sukhadeo Bere

Department of Computer Engineering, Dattakala Group Of Institution Faculty Of Engineering, Bhigwan-413130, University of Pune, Maharashtra, INDIA.

{Email}*aniketkrishnapatil309@gmail.com, nilimapawase31@gmail.com, shrutikasalunke2002@gmail.com, watharkarpragati@gmail.com

{Email}** tsnevase.foe@dattakala.edu.in, sachinbere@gmail.com

Abstract

The agricultural sector in India is characterized by unpredictable price fluctuations, lack of market transparency, and limited digital resources for farmers. Farm Mart is an innovative digital platform designed to bridge this gap by providing farmers with a direct-to-consumer marketplace. The system integrates machine learning-based price prediction models to forecast future crop prices. By combining robust backend architecture with intelligent analytics, Farm Mart empowers farmers to make informed selling decisions, reduces dependence on intermediaries, and enhances the overall agricultural supply chain efficiency. This paper discusses the system's design, implementation, and evaluation while comparing its performance with existing platforms like Krish-Bazar.