An Exploration of Various Deep Learning Techniques for Soil Classification

Soil classification is a critical task in agriculture and environmental monitoring, providing essential information about soil properties and their suitability for various uses. Traditional approaches rely on labor-intensive analyses, motivating the adoption of Deep learning (DL) techniques for soil classification using image data to automate and improve the process. This research investigates the application of DL and computer vision for soil classification using image data. Inspired by recent advancements, including smartphone-based soil sensors, RGB image classification under uncontrolled conditions, and lightweight CNN models, this research develops an optimized framework for soil classification using various performance metrics, robustness to environmental variations, and computational efficiency. The proposed approach results indicate the potential of DL-driven solutions to revolutionize soil analysis using DL techniques and support sustainable agricultural practices.