Abstract
Embedded systems are used in precision agriculture for data collection via sensors and for the control of actuators such as sprayers based on machine learning models. For plant classification and monitoring, it is easier to collect data of healthy plants than it is to collect data of plants that are infected by various diseases, because they are simply more common. Sufficient data are therefore often lacking for the accurate detection of diseased plants. In this paper, we outline an approach for the generation of synthetic data of infected plants that can be used to train a machine learning model for the classification of sugar beets. We use image augmentation techniques to build a pipeline that can automatically overlay diseased areas on healthy areas of leaf images.
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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Alao, O.B., Rother, K., Henkler, S. (2023). Synthetic Data for Machine Learning on Embedded Systems in Precision Agriculture. 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_11
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