We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP) so that the salient time step can be identified. We chose a selected number of such important time indices to create the bounding region of the shortest possible classification timeframe. We identified the period 21st April 2019 to 9th August 2019 as having the best trade-off in terms of accuracy and earliness. This timeframe only suffers a 0.75% loss in accuracy as compared to using the full timeseries. We observed that the LRP-derived important timesteps also highlight small details in input values that differentiates between different classes and

翻译：我们提出了一种通过可解释人工智能（XAI）方法识别关键时间步长以实现早期作物分类的方法。该方法首先训练一个基准作物分类模型，通过逐层相关性传播（LRP）识别显著时间步长。我们选取了若干此类重要时间索引，构建最短可行分类时间范围的边界区域。最终确定2019年4月21日至2019年8月9日期间为精度与时效性最优的平衡区间。与使用完整时间序列相比，该时间范围仅产生0.75%的精度损失。我们观察到，基于LRP识别的重要时间步长还突出了不同类别间输入值的微小差异，这些细节可有效区分不同作物类型。