Published Paper Details:

India is an agriculture-based country, with about 70% of households reliant on agriculture, and farmers dedicating their entire lives to the country's economy. They have a huge problem when the crop isn't worth the price and they aren't aware of the crop's marketplace price. Price changes in agricultural commodities have a negative impact on a country's GDP. Farmers are hurt emotionally and financially as their complete hard work for the crop cycle is for nothing. Price prediction has become a highly critical agricultural problem that can only be solved with the data available. The purpose of this study is to forecast crop prices for the next rotation. Price forecasting could aid the agriculture supply chain in making the appropriate decisions to reduce and manage the risk of price variations. This work is based on identifying appropriate data models that aid in achieving high price forecast accuracy and prediction. This paper describes a system that employs data analytics approaches to forecast crop prices. The suggested system will use machine learning algorithms and Sequence modeling to forecast crop prices based on a variety of characteristics such as harvested area, yield, and so on. This gives a farmer an idea of the future price of the crop that he has planted. This model entails crop price forecasting. Crop price forecasting is a difficult task since it involves so many variables.