Objekterkennung im Weinanbau – Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning
Heinrich K, Zschech P, Möller B, Breithaupt L, Maresch J (2019)
Publication Language: German
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 56
Pages Range: 964-985
Issue: 5
Journal Issue: 5
URI: https://link.springer.com/article/10.1365/s40702-019-00514-9
DOI: 10.1365/s40702-019-00514-9
Abstract
The transformation towards a digitized world introduces major changes to all economic sectors, among them the sector of agriculture, where intelligent information systems help to gather and analyze vast amounts of data to provide new business functions and models. Given this background, this article describes a big data analytics case study from the field of viticulture, where extensive image material was recorded using mobile recording devices in order to implement automated object detection to support operational vineyard activities, such as counting vines, identifying missing plants or predicting potential harvests. One of the challenges here was to correctly identify relevant wine objects such as vines, grapes and berries across their different hierarchical levels and to consistently count them in relation to moving image material. The authors provide a solution to those challenges by designing a data analysis process based on a deep learning framework for object detection. Additionally, the results as well as implications for the application of the proposed models in the field of agrarian management are discussed at the end of the article.
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Germany (DE)How to cite
APA:
Heinrich, K., Zschech, P., Möller, B., Breithaupt, L., & Maresch, J. (2019). Objekterkennung im Weinanbau – Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning. HMD : Praxis der Wirtschaftsinformatik, 56(5), 964-985. https://dx.doi.org/10.1365/s40702-019-00514-9
MLA:
Heinrich, Kai, et al. "Objekterkennung im Weinanbau – Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning." HMD : Praxis der Wirtschaftsinformatik 56.5 (2019): 964-985.
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