A Survey on a Prediction Model for Crop Productivity Optimization and Profit Maximization Using Market Trends and Topographical Factors

Abstract

Agricultural yield varies vastly with changes in topographical factors. Investing resources and effort on crops unmethodically can cause financial losses and disturb the supply and demand equilibrium. The market of crop produce is dynamic with unprecedented fluctuations due to economic and climatic factors. Climate change has altered the topography of practically every region across the globe. Factors like soil pH, precipitation, temperature, and humidity affect crop productivity in a multifold manner. The market price of certain produce have risen drastically from the past decade and of certain others have gone down. It is quintessential that the farmer understands the dynamics of the market and topography and invests in crops which could yield maximum profit. Traditional farming knowledge has not stood the test of time with the current economic and climatic conditions unforeseen and the impending uncertain. A predictive model is of demand which could conclusively establish the best crop to be sown at the inputted seasonal phase, topography, and market trends. This prediction model would be of critical importance to farmers and agriculturists. Random forest algorithm is used here to predict the crop productivity and market price.