The Crop Price Prediction Using Machine Learning: Preliminary Stage

Abstract

The objective of our research is mostly concerned with agriculture. Farmers are the key players
in agriculture. Knowing how much a crop will cost will enable you to make smarter judgments,
which will reduce the losses and lower the risk of price changes. An ML model that forecasts
agricultural prices in advance while accurately analyzing the crop may be able to solve this
issue. A predictive system, a statistical method combining machine learning and data collecting,
is used in many applications, including healthcare, retail, education, and government sectors.
Its usage in the agriculture sector has comparable relevance. The back-end predictive model
for this project is created utilizing machine learning algorithms. The steps involved in creating
a predictive model are data collecting, data cleaning, data mining, and validation. The goal is
to give farmers an intuitive user interface, and this model should correctly forecast crop market
value given the real-time variables provided.