Abstract
Plant phenotyping systems strive to maintain high categorization accuracy when expanding their scopes to larger environments. In this paper, we discuss problems associated with expanding the plant categorization scope. These problems are particularly complicated due to the increase in the number of species and the inter-species similarity. In our approach, we modify previously trained Convolutional Neural Networks (CNNs) and integrate domain-specific knowledge in the fine-tuning process of these models to maintain high accuracy while expanding the scope. This process is the key idea behind our CNN-based expanding approach resulting in plant-expert models. Experiments described in this paper compare the accuracy of an expanded phenotyping system using different plant-related datasets during the training of the CNN categorization models. Although it takes much longer to train these models, our approach achieves better performance compared to models trained without the integration of domain-specific knowledge, especially when the number of species increases significantly.
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Krause, J., Baek, K., Lim, L. (2020). Expanding CNN-Based Plant Phenotyping Systems to Larger Environments. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12540. Springer, Cham. https://doi.org/10.1007/978-3-030-65414-6_29
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