A Survey of Data Analytical Techniques on Commodity Price Prediction for Smart Agricultural System

Abstract

Purpose: Food or agricultural products are one of the most basic needs of people. The population of India and the rest of the world is growing at an exponential rate, as is the demand for food commodities. As a result, there should be a proper and convenient way to increase food production, as well as the introduction of efficient technologies in all aspects of the agriculture sector. Commodity prices are an important factor in agriculture because they determine the former's economic status and wealth. The farmer's income and profit are determined by the current and future price of the commodity. Farmers are losing a lot of money because they don't know what the price of their products will be in the future. As a result, there should be a proper approach that provides future information about agricultural products, allowing farmers to make decisions ahead of time before cultivating any product.

Design/Methodology/Approach: Developing a theoretical concept based on model building using the secondary sources and focus group interaction method and analysis of the model using the ABCD listing framework.

Findings/Result: A method known as price prediction uses historical and current data from a database to estimate future agricultural commodity prices. This paper was primarily concerned with identifying the appropriate data analysis techniques for implementing price prediction systems, particularly for agricultural products. Also conducts a survey on various predictive analytics approaches related to agricultural datasets. Finally, we used our own suggested model to implement a price prediction system with the help of a smart agricultural system.

Paper Type: Conceptual Research.