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Understanding Agricultural Landscape Dynamics with Explainable Artificial Intelligence
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2023
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Gesellschaft für Informatik e.V.
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Deep 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].