Honigeinlagerung

Der Wassergehalt des von den Pflanzen produzierten Nektars kann variieren. Um seine Konservierung in den Waben zu gewährleisten, darf er nicht gären. Deshalb wird er von den Bienen in Honig umgewandelt, indem sie seinen Wassergehalt senken und folglich den Zuckergehalt erhöhen. Dies verhindert die Entwicklung der Mikroorganismen. In der Dunkelheit des Bienenstocks ist es schwierig, den Herstellungsprozess des Honigs zu beobachten. Aus diesem Grund ist er auch nur wenig erforscht. Um die Umwandlung von Nektar in Honig zu untersuchen, haben wir die Technik der Tomographie eingesetzt. Sie ermöglicht es, den Zuckergehalt des eingelagerten Nektars sehr präzise zu messen, ohne den Bienenstock öffnen zu müssen und das Bienenvolk zu stören.

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Gallmann, J., Schüpbach B., Jacot-Ammann K., Albrecht M., Winizki J., Kirchgessner, N., Aasen, H.

Flower mapping in grasslands with drones and deep learning.

Frontiers in Plant Science, 12, 2022, Articolo 774965.

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Riassunto:

Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.

ISSN Online: 1664-462X
Digital Object Identifier (DOI): https://doi.org/10.3389/fpls.2021.774965
ID pubblicazione (Codice web): 48990 Inviare via e-mail

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