Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks

Abraham, Lizy and Davy, Steven and Zawish, Muhammad and Mhapsekar, Rahul and Finn, John A. and Moran, Patrick (2022) Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks. Sensors (Switzerland), 22 (6). ISSN 1424-8220

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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.

Item Type: Article
Additional Information: Funding Information: Funding: This research was supported by Science Foundation Ireland and the Department of Agriculture, Food and Marine on behalf of the Government of Ireland VistaMilk research centre under the grant 16/RC/3835. JAF and PM were supported by the SmartAgriHubs project, which received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 818182. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1600/1602
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Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:12
Last Modified: 04 Aug 2023 01:40

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