A Concept Study for Feature Extraction and Modeling for Grapevine Yield Prediction

Authors

  • Florian Huber Department of Computer Science IV, University of Bonn, Bonn, Germany
  • Benedikt Hofmann Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB), Karlsruhe, Germany
  • Hannes Engler Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding, Geilweilerhof, Siebeldingen, Germany
  • Pascal Gauweiler Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB), Karlsruhe, Germany
  • Benedikt Fischer Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB), Karlsruhe, Germany
  • Katja Herzog Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding, Geilweilerhof, Siebeldingen, Germany
  • Anna Kicherer Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding, Geilweilerhof, Siebeldingen, Germany
  • Reinhard Töpfer Julius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding, Geilweilerhof, Siebeldingen, Germany
  • Robin Gruna Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung (IOSB), Karlsruhe, Germany
  • Volker Steinhage Department of Computer Science IV, University of Bonn, Bonn, Germany

DOI:

https://doi.org/10.5073/vitis.2024.63.03

Keywords:

Yield forecasting, viticulture, Deep Learning, Extreme Gradient Boosting, XGBoost

Abstract

Yield prediction in viticulture is an especially challenging research direction within the field of yield prediction. The characteristics that determine annual grapevine yields are plentiful, difficult to obtain, and must be captured multiple times throughout the year. The processes currently used in grapevine yield prediction are based mainly on manually captured data and rigid statistical measures derived from historical insights. Experts for data acquisition are scarce, and statistical models cannot meet the requirements of a changing environment, especially in times of climate change. This paper contributes a concept on how to overcome those drawbacks, by (1) proposing a deep learning driven approach for feature recognition and (2) explaining how Extreme Gradient Boosting (XGBoost) can be utilized for yield prediction based on those features, while being explainable and computationally inexpensive. The methods developed will be influential for the future of yield prediction in viticulture.

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Published

2024-05-07

Issue

Vol. 63 (2024)

Section

Article

License

Copyright (c) 2024 The Author(s)

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