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

我们提出一种通过使用可解释的人工智能（XAI）方法来提前进行作物分类的方法。我们的方法包括训练一个基准作物分类模型，以进行层内相关传播（LRP），从而可以确定显著的时间步长。我们选择了一些重要的时间索引来创建可能的最短分类时间范围。我们确定了 2019 年 4 月 21 日至 2019 年 8 月 9 日的这段时间在准确性和提前性方面有着最佳平衡。与使用完整时间序列相比，这个时间范围仅损失了 0.75% 的准确性。我们观察到基于 LRP 得到的重要时间步长还突出了区分不同类别的输入值中的细小细节。