Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images
Species identification is critical for biological studies, ecological monitoring, and conservation efforts. A comprehensive comprehension of the evolutionary mechanisms that lead to biological variety is necessary while species are distinct categories of living organisms; however, naming, identifyin...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10648677/ |
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| author | Shaheer Habib Mubashir Ahmad Yasin Ul Haq Rabia Sana Asia Muneer Muhammad Waseem Muhammad Salman Pathan Soumyabrata Dev |
| author_facet | Shaheer Habib Mubashir Ahmad Yasin Ul Haq Rabia Sana Asia Muneer Muhammad Waseem Muhammad Salman Pathan Soumyabrata Dev |
| author_sort | Shaheer Habib |
| collection | DOAJ |
| description | Species identification is critical for biological studies, ecological monitoring, and conservation efforts. A comprehensive comprehension of the evolutionary mechanisms that lead to biological variety is necessary while species are distinct categories of living organisms; however, naming, identifying, and differentiating between species is more complex than it may seem. Traditional methods, relying on dichotomous keys and manual observation, are time-consuming and error-prone. Precise species identification is crucial for all taxonomic investigations and biological procedures. Numerous experts are currently engaged in the task of identifying a solitary species. To address these challenges, we present a robust artificial intelligence framework for species identification using deep learning techniques, specifically leveraging the ResNet-50 Convolutional Neural Network (CNN). Our approach utilizes a ResNet-50-based CNN to accurately classify 15 species, including humans, plants, and animals, from images taken at unique locations and angles. The dataset was pre-processed and augmented to enhance training, ensuring robustness against variations in lighting, occlusion, and background clutter. Featuring 4 million trainable parameters, our modified ResNet-50 model demonstrated superior computational efficiency and accuracy. The proposed model achieved an overall accuracy of 96.5%, with class-specific accuracies of 98.25% for humans, 97.81% for animals, and 96.90% for plants. These results surpass those of existing models such as GoogleNet, VGG, SegNet, and DeepLab v3+, highlighting the efficacy of our approach. Performance was evaluated using metrics such as sensitivity, specificity, and error rate, further validating its reliability. Our findings suggest that the ResNet-50-based CNN model is highly effective for automatic species identification, offering significant improvements in accuracy and computational efficiency. |
| format | Article |
| id | doaj-art-e784982108d5464eb634f3c0c78c3b66 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e784982108d5464eb634f3c0c78c3b662025-08-20T01:47:50ZengIEEEIEEE Access2169-35362024-01-011214671814673210.1109/ACCESS.2024.345001610648677Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural ImagesShaheer Habib0Mubashir Ahmad1https://orcid.org/0000-0002-4542-1808Yasin Ul Haq2https://orcid.org/0000-0002-8499-9039Rabia Sana3Asia Muneer4https://orcid.org/0000-0002-0932-3848Muhammad Waseem5Muhammad Salman Pathan6Soumyabrata Dev7https://orcid.org/0000-0002-0153-1095Department of Computer Science, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Science, COMSATS University Islamabad, Abbottabad, PakistanDepartment of Computer Science and Engineering, University of Engineering and Technology, Lahore, Narowal, PakistanDepartment of Computer Science and Engineering, University of Engineering and Technology, Lahore, Narowal, PakistanDepartment of Computer Science and IT, University of Sargodha, Sargodha, PakistanSchool of Computing, Dublin City University, Dublin 9, IrelandDepartment of Computer Science, University of Engineering and Technology, Lahore, PakistanSchool of Computer Science, University College Dublin, Dublin 4, IrelandSpecies identification is critical for biological studies, ecological monitoring, and conservation efforts. A comprehensive comprehension of the evolutionary mechanisms that lead to biological variety is necessary while species are distinct categories of living organisms; however, naming, identifying, and differentiating between species is more complex than it may seem. Traditional methods, relying on dichotomous keys and manual observation, are time-consuming and error-prone. Precise species identification is crucial for all taxonomic investigations and biological procedures. Numerous experts are currently engaged in the task of identifying a solitary species. To address these challenges, we present a robust artificial intelligence framework for species identification using deep learning techniques, specifically leveraging the ResNet-50 Convolutional Neural Network (CNN). Our approach utilizes a ResNet-50-based CNN to accurately classify 15 species, including humans, plants, and animals, from images taken at unique locations and angles. The dataset was pre-processed and augmented to enhance training, ensuring robustness against variations in lighting, occlusion, and background clutter. Featuring 4 million trainable parameters, our modified ResNet-50 model demonstrated superior computational efficiency and accuracy. The proposed model achieved an overall accuracy of 96.5%, with class-specific accuracies of 98.25% for humans, 97.81% for animals, and 96.90% for plants. These results surpass those of existing models such as GoogleNet, VGG, SegNet, and DeepLab v3+, highlighting the efficacy of our approach. Performance was evaluated using metrics such as sensitivity, specificity, and error rate, further validating its reliability. Our findings suggest that the ResNet-50-based CNN model is highly effective for automatic species identification, offering significant improvements in accuracy and computational efficiency.https://ieeexplore.ieee.org/document/10648677/Convolutional neural network (CNN)specie recognitionRes-Net-50artificial intelligence |
| spellingShingle | Shaheer Habib Mubashir Ahmad Yasin Ul Haq Rabia Sana Asia Muneer Muhammad Waseem Muhammad Salman Pathan Soumyabrata Dev Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images IEEE Access Convolutional neural network (CNN) specie recognition Res-Net-50 artificial intelligence |
| title | Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images |
| title_full | Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images |
| title_fullStr | Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images |
| title_full_unstemmed | Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images |
| title_short | Advancing Taxonomic Classification Through Deep Learning: A Robust Artificial Intelligence Framework for Species Identification Using Natural Images |
| title_sort | advancing taxonomic classification through deep learning a robust artificial intelligence framework for species identification using natural images |
| topic | Convolutional neural network (CNN) specie recognition Res-Net-50 artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10648677/ |
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