Advancements in Soil Classification: An In-Depth Analysis of Current Deep Learning Techniques and Emerging Trends

Abstract

Soil classification is essential to agriculture, environmental management, and civil engineering. In the recent past, deep-learning algorithms have had a profound effect on solving research challenges in various fields. Soil science has also benefited substantially from the implementation of time-saving and accurate deep-learning models. Deep learning techniques have demonstrated their potential for precisely classifying soil classes and predicting soil properties. In this paper, we provide a thorough analysis of the current techniques for classifying soil using deep learning models. We also discuss emerging trends in soil classification research, such as the use of lightweight models, multi-task learning models, and transfer learning. The advancements in soil classification were the focal point of this study. The study evaluates 12 research papers out of 150 publications initially retrieved from scholarly databases pertaining to the topic. The academic publications on deep learning models for soil classification were culled based on a set of predetermined criteria for obtaining the most profound understanding. To establish an effective method for soil identification, it is necessary to extract the features from the input images with care. This review suggests additional potential deep learning models that can be anticipated for improved soil identification results. The analysis concludes with an examination of the technical and non-technical errors made by scholars and a discussion of the ramifications for the future of soil identification. In addition, we emphasize the difficulties in soil classification, such as data imbalance, limited data availability, and the interpretability of models, and suggest potential solutions to resolve these issues. This in-depth analysis could support soil science researchers to propose more effective methods for soil identification.