Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks
Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore there is a strong need for digital methods to improve...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/6/2190 |
| _version_ | 1827625698111520768 |
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| author | Lizy Abraham Steven Davy Muhammad Zawish Rahul Mhapsekar John A. Finn Patrick Moran |
| author_facet | Lizy Abraham Steven Davy Muhammad Zawish Rahul Mhapsekar John A. Finn Patrick Moran |
| author_sort | Lizy Abraham |
| collection | DOAJ |
| description | Ireland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore there is a strong need for digital methods to improve the speed and efficiency of identification and differentiation of farmland habitats. This is challenging because of the large number of subcategories having nearly indistinguishable features within the habitat classes. Heterogeneity among sites within the same habitat class is another problem. Therefore, this research work presents a preliminary technique for accurate farmland classification using stacked ensemble deep convolutional neural networks (DNNs). The proposed approach has been validated on a high-resolution dataset collected using drones. The image samples were manually labelled by the experts in the area before providing them to the DNNs for training purposes. Three pre-trained DNNs customized using the transfer learning approach are used as the base learners. The predicted features derived from the base learners were then used to train a DNN based meta-learner to achieve high classification rates. We analyse the obtained results in terms of convergence rate, confusion matrices, and ROC curves. This is a preliminary work and further research is needed to establish a standard technique. |
| first_indexed | 2024-03-09T12:42:17Z |
| format | Article |
| id | doaj.art-d8bb2e4f8af34353ba6936589a544775 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:42:17Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d8bb2e4f8af34353ba6936589a5447752023-11-30T22:17:29ZengMDPI AGSensors1424-82202022-03-01226219010.3390/s22062190Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural NetworksLizy Abraham0Steven Davy1Muhammad Zawish2Rahul Mhapsekar3John A. Finn4Patrick Moran5Walton Institute for Information and Communication Systems Science, Waterford Institute of Technology, X91 WR86 Waterford, IrelandWalton Institute for Information and Communication Systems Science, Waterford Institute of Technology, X91 WR86 Waterford, IrelandWalton Institute for Information and Communication Systems Science, Waterford Institute of Technology, X91 WR86 Waterford, IrelandWalton Institute for Information and Communication Systems Science, Waterford Institute of Technology, X91 WR86 Waterford, IrelandTeagasc, Environment Research Centre, Johnstown Castle, Y35 TC97 Wexford, IrelandForest Environmental Research and Services Ltd. (FERS), Kilberry, C15 R6Y3 Navan, IrelandIreland has a wide variety of farmlands that includes arable fields, grassland, hedgerows, streams, lakes, rivers, and native woodlands. Traditional methods of habitat identification rely on field surveys, which are resource intensive, therefore there is a strong need for digital methods to improve the speed and efficiency of identification and differentiation of farmland habitats. This is challenging because of the large number of subcategories having nearly indistinguishable features within the habitat classes. Heterogeneity among sites within the same habitat class is another problem. Therefore, this research work presents a preliminary technique for accurate farmland classification using stacked ensemble deep convolutional neural networks (DNNs). The proposed approach has been validated on a high-resolution dataset collected using drones. The image samples were manually labelled by the experts in the area before providing them to the DNNs for training purposes. Three pre-trained DNNs customized using the transfer learning approach are used as the base learners. The predicted features derived from the base learners were then used to train a DNN based meta-learner to achieve high classification rates. We analyse the obtained results in terms of convergence rate, confusion matrices, and ROC curves. This is a preliminary work and further research is needed to establish a standard technique.https://www.mdpi.com/1424-8220/22/6/2190farmland habitathabitat classificationdrone imagesbase-learnermeta-learnerstacked ensemble model |
| spellingShingle | Lizy Abraham Steven Davy Muhammad Zawish Rahul Mhapsekar John A. Finn Patrick Moran Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks Sensors farmland habitat habitat classification drone images base-learner meta-learner stacked ensemble model |
| title | Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks |
| title_full | Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks |
| title_fullStr | Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks |
| title_full_unstemmed | Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks |
| title_short | Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks |
| title_sort | preliminary classification of selected farmland habitats in ireland using deep neural networks |
| topic | farmland habitat habitat classification drone images base-learner meta-learner stacked ensemble model |
| url | https://www.mdpi.com/1424-8220/22/6/2190 |
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