Soil Type Prediction Using Machine Learning and Deep Learning Models for Multi-domain Applications

Soil classification plays a significant function in multi-disciplinary areas like agriculture, biotechnology, and civil engineering. Precise identification of soil groups is essential to increase agricultural production, support microbial and genetic research, and guarantee structural integrity during construction. Conventional soil classification depends significantly on field observation and laboratory testing, which are time-consuming, labor-intensive, and subjective. Advances in machine learning (ML) and computer vision over the last few years have opened up new possibilities for automated soil type prediction [1], [2]. In this paper, a two-pronged approach framework employing feature-based ML models and a scratch-trained bespoke convolutional neural network (CNN) is presented for soil classification. Handcrafted features such as color histograms, Local Binary Patterns (LBP), and Gray Level Co-occurrence Matrix (GLCM) descriptors are extracted and evaluated using Support Vector Machines (SVM) and Random Forest (RF) classifiers in the baseline approach. Since handcrafted features are limiting, a CNN model is developed to learn discriminative features directly from soil images automatically, without the need for pre-trained networks. The experimental results on an open dataset of five classes of soils illustrate the performance of the two approaches, and CNN outperformed traditional ML algorithms. The proposed system reveals the capability of autonomous soil prediction systems to enhance agricultural production, to direct biotechnological research, and to enable secure land use in construction.