Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks

AI-based decision support can help farmers to reach improved productivity in an environmentally sustainable way. Through transfer learning, an existing Convolutional Neural Network is progressively adapted to provide high quality forecasting results using agricultural time series in the context of different locations, growth and soil types, climate zones, and management variations. The delivered results are validated by appropriate statistical methods and show improved prediction accuracy.

Münzberg,Alexander; Troost,Christian; Bernardi,Ansgar (2022): Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks. INFORMATIK 2022. DOI: 10.18420/inf2022_128. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-720-3. pp. 1495-1503. Künstliche Intelligenz in der Umweltinformatik (KIU-2022). Hamburg. 26.-30. September 2022

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Decision Support in Agriculture , AI-based Methods , Transfer Learning , Time Series Analysis , Convolutional Neural Network

DOI

10.18420/inf2022_128

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften

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Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks

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