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

Huber, Florian; Hofmann, Benedikt; Engler, Hannes; Gauweiler, Pascal; Fischer, Benedikt; Herzog, Katja; Kicherer, Anna; Töpfer, Reinhard; Gruna, Robin; Steinhage, Volker

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.

FEATURE extraction; STATISTICAL models; GRAPES; STATISTICS; ACQUISITION of data; DEEP learning

Vitis, 2024, Vol 63, p1

0042-7500

Academic Journal

10.5073/vitis.2024.63.03