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

Zusammenfassung
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.
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  • BibTeX
Münzberg, Al., Troost, Ch. & Bernardi, An., (2022). Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks. In: Demmler, D., Krupka, D. & Federrath, H. (Hrsg.), INFORMATIK 2022. Gesellschaft für Informatik, Bonn. (S. 1495-1503). DOI: 10.18420/inf2022_128
@inproceedings{mci/Münzberg2022,
author = {Münzberg,Alexander AND Troost,Christian AND Bernardi,Ansgar},
title = {Using Transfer Learning for Quality Improved Forecasting of Temporal Agricultural Processes by Adapting Convolutional Neural Networks},
booktitle = {INFORMATIK 2022},
year = {2022},
editor = {Demmler, Daniel AND Krupka, Daniel AND Federrath, Hannes} ,
pages = { 1495-1503 } ,
doi = { 10.18420/inf2022_128 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
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