Stiller, StefanStolzenburg, Frieder2023-09-202023-09-202023https://dl.gi.de/handle/20.500.12116/42397Deep learning (DL) models, particularly those utilizing computer vision techniques such as proximal and remote sensing imagery, have witnessed extensive utilization within agriculture [KPB18]. These DL applications encompass diverse areas, including land cover and crop type mapping [Ku17], crop yield estimation [KS15, NNL19], drought monitoring [Sh19], plant disease spread analysis [Te20], and overall monitoring of agricultural systems. DL applications offer significant potential to enhance agricultural practices at various scales, spanning from individual organisms, field, landscape, to regional and even continental scales [Ry22b].endeep learning; precision agriculture; crop yield; explainable artificial intelligence; spatial cross validation; self-supervised learningText10.18420/ki2023-dc-09