Exploring Machine Learning Models for Soil Nutrient Properties Prediction: A Systematic Review
Olusegun Folorunso, Oluwafolake Ojo, Mutiu Abolanle Busari, Muftau Adebayo, Adejumobi Joshua, Daniel Folorunso, Charles Okechukwu Ugwunna, Olufemi Olabanjo, Olusola Olabanjo
Abstract
Agriculture is essential to a flourishing economy. Although soil is essential for sustainable food production, its quality can decline as cultivation becomes more intensive and demand increases. The importance of healthy soil cannot be overstated, as a lack of nutrients can significantly lower crop yield. Smart soil prediction and digital soil mapping offer accurate data on soil nutrient distribution needed for precision agriculture. Machine learning techniques are now driving intelligent soil prediction systems. This article provides a comprehensive analysis of the use of machine learning in predicting soil qualities. The components and qualities of soil, the prediction of soil parameters, the existing soil dataset, the soil map, the effect of soil nutrients on crop growth, as well as the soil information system, are the key subjects under inquiry. Smart agriculture, as exemplified by this study, can improve food quality and productivity.