Forecasting of Optimal Crop Yield through Data Mining Algorithm

Abstract

The initial stage of analytics is descriptive analytics. It is a method via which we can learn about the past. We are aware that the past serves as the finest indicator of the upcoming. That is study, Descriptive analytics are used by us to estimate crop yields effectively for the production of sugarcane in agriculture. Three datasets—the soil, rainfall, and yield datasets—are used in this paper. In order to determine the real estimated cost and the accuracy of various strategies, we create a composite dataset and then apply a number of supervised techniques to it. Three supervised techniques—crop prediction, provision direction, and LSS method—are employed in this research. It is a comparative study that reveals the mistake rate and accuracy of training the suggested model. The training model should be more accurate and have a lower error rate than possible. Additionally, the suggested model can provide the real cost of the projected crop production and can be classified as LOW, MID, or HIGH.