Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh.
Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected from a particular region. An image dataset has bee...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0328102 |
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| Summary: | Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected from a particular region. An image dataset has been developed for wood species classification of Bangladeshi forest. Our aim is to address the gaps by comparing and contrasting our developed sequential Convolutional Neural Network based BdWood model with several deep learning, ensemble technique, Machine learning classification models on specific wood species identification for Bangladeshi forests. Using our own dataset, comprising more than 7119 high-quality captured images representing seven types of wood species of Bangladesh. It is found that DenseNet121 is the clear winner in our thorough evaluation among seven pre-trained models. The highest accuracy of DenseNet121 is achecived 97.09%. In addition, our customized BdWood model, which is adapted to the desired outcome, produced results that are excellent. BdWood model achieves a training accuracy of 99.80%, validation accuracy of 97.93%, an F1-score of 97.94%, and an outstanding ROC-AUC of 99.85%, demonstrating its effectiveness in wood species classification. Gradient-weighted Class Activation Mapping (Grad CAM) is used to interpret the model's predictions, providing insights into the features contributing to the classification decisions. Finally, to make our research practically applicable, we have also developed an Android application as a tangible outcome of this work. |
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| ISSN: | 1932-6203 |