In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images are provided in the challenge; (2) samples are equipped with their collection dates; (3) the samples of different dates show notable discrepancies. Based on the findings, we partition the dataset into discrete groups by the dates and train models on each divided group. We then adopt the pseudo-labeling approach to label the test data and incorporate those with high confidence into the training set. In pseudo-labeling, we leverage models ensemble with different architectures to enhance the reliability of predictions. The pseudo-labeling and ensembled model training are iteratively conducted until all test samples are labeled. Finally, the separated models for each group are unified to obtain the model for the whole dataset. Our method achieves an average of 93.6\% Top-1 test accuracy~(94.0\% on WW2020 and 93.2\% on WR2021) and wins the 1$st$ place in the Deep Nutrient Deficiency Challenge~\footnote{https://cvppa2023.github.io/challenges/}.

翻译：在小麦营养缺乏症分类挑战中，我们提出分而集成（DEEM）方法用于渐进式测试数据预测。我们发现：（1）挑战提供了测试图像；（2）样本带有采集日期；（3）不同日期的样本存在显著差异。基于此，我们按日期将数据集划分为离散组，并在每个分组上训练模型。随后采用伪标签方法标注测试数据，将高置信度样本纳入训练集。在伪标签过程中，我们利用不同架构的模型集成提升预测可靠性。迭代执行伪标签与集成模型训练，直至所有测试样本均被标注。最终，统一各分组独立模型以得到覆盖完整数据集的模型。该方法平均测试Top-1准确率达93.6%（WW2020上94.0%，WR2021上93.2%），在深度营养缺乏症挑战赛1中荣获第一名\footnote{https://cvppa2023.github.io/challenges/}。