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AGRICULTURE CROP PRICE PREDICTION USING MACHINE
LEARNING

 

 

Authors:
Ajinkya Bhatakar1, Lalit Tayde2, Sandesh Raut3, Ankit Pakhare4, Tejas Bavaskar5, Shivaji Chavhan6

1 Assistant Professor, Computer Science & Engg Department, VBKCOE, Malkapur, Maharashtra, India
2 Student, Computer Science & Engg Department, VBKCOE, , Malkapur, Maharashtra, India
3 Student, Computer Science & Engg Department, VBKCOE, , Malkapur, Maharashtra, India
4 Student, Computer Science & Engg Department, VBKCOE, , Malkapur, Maharashtra, India
5Student, Computer Science & Engg Department, VBKCOE, , Malkapur, Maharashtra, India
6Student, Computer Science & Engg Department, VBKCOE, , Malkapur, Maharashtra, India

Abstract - The accurate forecast of crop prices is of considerable importance to farmers, policymakers, and stakeholders to enable informed decisions and ensure economic stability in the agricultural sector. The traditional forecasting methods are largely ineffective when it comes to accurate forecasting, due to the complex and dynamic nature of the agricultural market. This study proposes a machine learning based solution to effectively forecast crop prices. Various machine learning models such as regression, decision trees, and neural networks were used to analyze and forecast crop prices using historical data, weather patterns, market trends, and other relevant factors. The focus is on increasing prediction accuracy, reducing uncertainty, and helping sustainable agricultural usage of resources. The results show that machine learning models perform better than traditional means of forecasting in general and have a higher adaptability toward dynamic market conditions. The findings of this study point to the immense potential machine learning has in transforming agricultural price forecasting, which ultimately shall allow better resource allocation and better decision-making processes. The research adds to the existing body of knowledge regarding the applications of machine learning in agriculture, and it offers scalable and efficient solutions for crop price forecasting. In the concluding lines, the study said that the participation of machine learning in agricultural forecasting can drastically benefit farmers and stakeholders by promoting economic stability and sustainability in this care.

Keywords: Crop Price Prediction, Machine Learning, Agricultural Forecasting, Regression Models, Neural Networks, Decision Trees, Sustainable Agriculture.

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