Abstract
Neural Networks (NNs) are used in various application areas to identify objects. Reliable behavior of NNs is an important aspect, especially for embedded systems. In this paper, we focus on the analysis of NNs to find correlations between their characteristics in order to be reliable and predictable in time, with the aim of making sugar beet recognition more transparent. Although obtaining promising results, as we are only going to focus on analysing models and finding correlations between some of their characteristics, this paper is only the first milestone towards using this correlation to optimize smart farming applications to improve the production and sustainability of the plantations around the world.
This research is supported by a grant from the Ministry of Economic Affairs, Industry, Climate Action and Energy of the State of North Rhine-Westphalia (MWIDE) as part of the 5G-Landwirtschaft-ML project in the context of the program 5G.NRW (01.05.2022 - 31.12.2024, grant number 005-2108-0039).
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Brodo, L., Henkler, S., Rother, K. (2023). Analysing the Characteristics of Neural Networks for the Recognition of Sugar Beets. In: Henkler, S., Kreutz, M., Wehrmeister, M.A., Götz, M., Rettberg, A. (eds) Designing Modern Embedded Systems: Software, Hardware, and Applications. IESS 2022. IFIP Advances in Information and Communication Technology, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-34214-1_10
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