An ensemble deep learning approach for soil pH classification leveraging GLCM-based texture features and neural networks
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Abstract
Soil pH-level classification is a critical challenge in precision agriculture owing to data scarcity, high intra-class similarity, and subtle visual variations between pH levels. This research proposed an ensemble-feature deep learning framework that integrates convolutional neural networks with gray-level co-occurrence matrix (GLCM) texture descriptors to address fundamental limitations in pH-level classification. Through comprehensive experiments, we evaluated diverse architectures (i.e., InceptionV3, Inception ResNetV2, ResNet50, and VGG16) enhanced by GLCM features, demonstrating a substantial increase in classification accuracy, particularly under minimal visual distinctions between pH levels. The optimal Inception ResNetV2 w/GLCM configuration achieved the best overall performance, with an F1-score of 82.10%, an accuracy of 82.22%, and a precision of 82.01%, outperforming the conventional machine learning and deep learning approaches evaluated in this study. The proposed method innovatively provides a synergistic integration of hierarchical deep learning features with statistical texture analysis, achieving robust discrimination of visually similar soil sample images capturing different pH levels. These findings advance automated soil analysis and contribute to precision agriculture by establishing a robust solution for image-based soil pH-level classification.
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