Abstract
Deep Learning (DL) has become popular due to its ease of use and accuracy, with Transfer Learning (TL) effectively reducing the number of images needed to solve environmental problems. However, this approach has some limitations which we set out to explore: Our goal is to detect the presence of an invasive blueberry species in aerial images of wetlands. This is a key problem in ecosystem protection which is also challenging in terms of DL due to the severe imbalance present in the data. Results for the ResNet50 network show a high classification accuracy while largely ignoring the blueberry class, rendering these results of limited practical interest to detect that specific class. Moreover, by using loss function weighting and data augmentation results more akin to our practical application, our goals can be obtained. Our experiments regarding TL show that ImageNet weights do not produce satisfactory results when only the final layer of the network is trained. Furthermore, only minor gains are obtained compared with random weights when the whole network is retrained. Finally, in a study of state-of-the-art DL architectures best results were obtained by the ResNeXt architecture with 93.75 True Positive Rate and 98.11 accuracy for the Blueberry class with ResNet50, Densenet, and wideResNet obtaining close results.
Highlights
Recent changes in global climate conditions influence species composition and increase the impact of invasive plant species in natural environments
Experiments were presented using real data corresponding to three orthomosaics constructed using the Unmanned aerial vehicles (UAV) data acquired
We have shown that Deep Learning (DL) networks can be used to detect the presence of invasive blueberry bushes in German wetlands
Summary
Recent changes in global climate conditions influence species composition and increase the impact of invasive plant species in natural environments. Invasive species are suspected to decrease biodiversity and ecosystem degradation [2]. Their dominance over native species might result in a displacement of native species, multiple stress factors on ecosystems, and economic costs due to losses in agriculture and forestry [3]. The need to precisely understand the ecological impacts of invasive species in ecosystems has become a key issue when designing and prioritizing natural resource management approaches [2]. Such land use and nature conservation management approaches should deal with the prevention, early detection and reduction of invasive species with minimum cost. Existing studies are limited in time and area studied due to the use of costly and labor-intensive field surveys [2]
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