Soil Type Identification via Deep Learning and Machine Learning Methods

Abstract

Soil type identification stands as a crucial concern in numerous countries, to ensure optimal crop yield, farmers need to accurately identify the suitable soil type for specific crops, which plays a significant role in meeting the heightened global food demand. The objective of this survey paper is to present a thorough and up-to-date overview of prevailing methodologies in soil identification, primarily focusing on image analysis, machine learning, and deep learning techniques. The paper initiates by highlighting the significance of soil identification and the limitations inherent in traditional methods. It then delves into the fundamental principles of image processing, deep learning, and spectroscopy, explaining how these techniques can be applied to soil identification. The survey presents an in-depth analysis of various image processing techniques employed for soil identification, including image segmentation, feature extraction, and classification algorithms. Furthermore, it discusses the application of deep learning models for soil classification based on image data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.