A Review of Machine Learning and IoT Approaches for the Soil Quality Assessment in Agricultural and Land Management

This research paper examines several soil qualities, such as texture, structure, density, porosity, wetness, and pH, and how these affect agricultural productivity and soil management. It discusses how to analyze soil texture using mechanical screening and sedimentation techniques, as well as the link between soil density and pore space. The paper highlights the importance of soil moisture in plant growth and discusses various methods for monitoring soil moisture, including remote sensing. Additionally, it looks into how soil pH affects plant health and offers helpful suggestions for adjusting soil pH to maximize growing conditions. The literature review focuses on advances in machine learning and IoT technologies that improve soil analysis and prediction, emphasizing its potential for precision agriculture and sustainable land management. The conclusion emphasizes the need of understanding soil parameters in order to maximize agricultural outputs and the usefulness of advanced modelling approaches in establishing sustainable agricultural practices. This study finally demonstrates the importance of exact soil data for good agricultural decision-making and environmental management.