Water Level Prediction using Random Forest Algorithm

Abstract

Agriculture is a main occupation of the global economy, and one important factor that must be considered is the level of irrigation. The primary goal is to present a novel application of technology in agriculture. Technology can advance agriculture and enhance its output. Advanced technology like machine learning will be used to address the issue of crop water requirements. An efficient method to build a model to predict how much water is required to produce crops efficiently and healthily is provided by machine learning. This includes a comparison between machine learning methods like decision trees, Random Forest regressors by passing parameters like crop type, soil type, Region, Temperature and weather conditions. Machine learning techniques like decision trees and Random Forest regressors are compared using parameters like crop type, soil type, region, temperature, and weather. This also discusses the need for crop water; it excludes other agricultural processes, such as crop type prediction. A Machine learning model which forecasts how much water is needed for crops is discussed in this research study. This study will help researchers, farmers, agriculture students, and other non-researchers who are interested in learning more about machine learning advancements in agriculture.