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Crop Yield Prediction: an Operational Approach to Crop Yield Modeling on Field and Sub-Field Level with Machine Learning Models

Patrick Helber; Benjamin Bischke; Peter Habelitz; Cristhian Sanchez; Deepak Kumar Pathak; Miro Miranda Lorenz; Hiba Najjar; Francisco Mena; Jayanth Siddamsetty; Diego Arenas; Michaela Vollmer; Marcela Charfuelan Oliva; Marlon Nuske; Andreas Dengel
In: 2023 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE International Geoscience and Remote Sensing Symposium (IGARSS-2023), July 16-21, Pasadena, CA, USA, IEEE, 10/2023.


Accurate and reliable crop yield prediction is a complex task. The yield of a crop depends on a variety of factors whose accurate measurement and modeling is challenging. At the same time, reliable yield prediction is highly desirable for farmers to optimize crop production. In this paper, we intro- duce a modeling based on remote sensing data and Machine Learning models evaluated on a large-scale dataset to address the challenge of an operational crop yield estimation and fore- casting on fi eld and subfi eld level. With our approach, we aim towards a global yield modeling based on Machine Learning models which operates across crop types without the need for crop-specifi c modeling. We demonstrate that our approach learns to map in-fi eld variability for all studied crop types. Overall, the predictions have an error (RRMSE) of around 15% and an R 2 value of 0.77 at fi eld level.


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