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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Optimal multispectral sensor confgurations through machine learning for cognitive agriculture
Fraunhofer-Gesellschaft
2021
,
Becker, Florian
,
Backhaus, Andreas
,
Johrden, Felix
,
Flitter, Merle
Hyperspectral sensor systems play a key role in the automation of work processes in the farming industry. Non-invasive measurements of plants allow for an assessment of the vitality and health state and can also be used to classify weeds or infected parts of a plant. However, one major downside of hyperspectral cameras is that they are not very cost-effective. In this paper, we show, that for specific tasks, multispectral systems with only a fraction of the wavelength bands and costs of a hyperspectral system can lead to promising results for regression and classification tasks. We conclude that for the ongoing automation efforts in the context of cognitive agriculture reduced multispectral systems are a viable alternative.