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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.

Zusammenfassung

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].

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Stiller, Stefan (2023): Understanding Agricultural Landscape Dynamics with Explainable Artificial Intelligence. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-09. Gesellschaft für Informatik e.V.. pp. 77-87. Doctoral Consortium at KI 2023. Berlin. 45195

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