Machine Learning and Statistical Approaches used in Estimating Parameters that Affect the Soil Fertility Status: A Survey

Soil is called as the skin of the earth and is greatly influenced by geographical, environmental and weather parameters. Its rich nutrient and mineral content, plays an eminent role in regulating the essence of the ecosystem. Various studies were carried out by the researchers in predicting different parameters for knowing the characteristics of the soil-its nutrient and mineral content- and their usefulness in finding the soil fertility status. There are so many parameters available and their contribution in predicting the soil fertility is a cumbersome job for the agriculture scientist, where automated analytical process plays a major role. The machine learning approaches combined with statistical inferences brings out the novel ways in improving the accuracy of prediction by identifying the important attributes of soil fertility. In this paper, a study is made on different parameters used in the literature for defining the characteristics of the soil and how they are used as input for machine learning algorithms/analysis for predicting the soil fertility. Based on this study, it could be observed that prediction techniques could be efficiently applied over optimized soil parameters for soil fertility prediction with more accuracy and less human intervention.