Soil Texture Prediction: Advances in Remote Sensing, Image Analysis, and Machine Learning

Soil texture is critical in soil behavior regulation since it controls its physical and chemical biological properties. The properties determine plant development, water absorption capabilities, and natural nutrient cycles. The accuracy of hydrometer and pipette methods remains authentic, but their implementation proves to be unscalable due to expensive equipment requirements, high complexity, and lengthy processing times. Soil texture predictions became feasible because of advancements in remote sensing, digital image processing, and machine learning (ML) technology, which allow both high-scale prediction and low-cost and fast execution. An analysis examines how satellite information merges with proximal sensing through applicable ML models such as RF, SVM, CNNs, and hybrid deep learning techniques for conducting soil texture predictions. The paper evaluates regional and global examples with Land Use/Cover Area Frame Survey (LUCAS) datasets and Soil Grids and Indian Soil Health Card (ISRIC) schemes while discussing the benefits of smartphone-based systems. The article presents technical comparisons before exploring obstacles and prospective developments, including integrating IoT and AI-based frameworks.The review shows how new methods help sustainable agriculture, precision farming, and environmental protection through precise big-scale mapping of soil textures that support decision-making.