Convolutional Neural Network for Soil Surface Image Classification in Six Soil Categories
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Abstract
Soil type classification is important for agriculture, geology, and civil engineering because soil characteristics influence land suitability, tillage strategy, irrigation, fertilization, and foundation stability. However, manual soil identification through field observation or laboratory analysis can be time-consuming and may introduce subjective errors. This study proposes an automated soil image classification approach using a Convolutional Neural Network (CNN). The dataset comprises six soil categories-black soil (tanah hitam), yellow soil (tanah kuning), peat soil (tanah gambut), cinder/volcanic soil (tanah vulkanik), laterite soil (tanah laterit), and cracked soil (tanah retak) -collected from a public Kaggle dataset and complemented with web-extracted cracked-soil images. Images are preprocessed through resizing, normalization, and training-time augmentation before being split into training, validation, and testing subsets. Experimental results show that the proposed CNN achieves 91.61% test accuracy and substantially improves performance compared to training without preprocessing. These findings indicate that CNN-based models, supported by appropriate preprocessing, can provide practical decision support for rapid soil type identification under diverse image conditions.
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