Price Prediction Model of fruits, Vegetables and Pulses according to Weather

Considering the profit component in agriculture, particularly in India, where more than 33% of the population lives in poverty. Temperature, humidity, ph, rainfall, and other climatic conditions influence agricultural yields, causing the price of fruits, vegetables, and pulses to fluctuate, most likely increasing. The farmers are need to know what types of crops they may grow and get profit. Harvest yield estimation and evaluation are done on a territorial basis across the world to enable high yield and cost estimation. There is no structure in place to advise farmers on which crops to cultivate and what price they may earn for each commodity. As a result, the proposed method attempted to anticipate the price of fruits, vegetables, and pulses that a farmer may receive from his field in this article by examining trends in prior data. For our investigation, the proposed method looked at a few fruits, vegetables, and pulses. The proposed method employed a variety of variables, including ph, humidity, precipitation, temperature, and market pricing. In this proposed method Decision Tree Regression method is being used, a supervised machine learning algorithm that analyses the data and makes predictions for a fresh batch of data. The proposed method also provides a time series analysis of the price and gain for the upcoming year compared to the previous year.