Recently, image based plant phenotyping is used extensively for plant trait estimation, plant accession classification and selection, and plant stress analysis. Plant accession classification and selection helps in identification of plants tolerant to local climatic conditions. Over the past few years, convolutional neural network (CNN) models were predominantly used for classification of plants and plant diseases from static plant images using spatial features. In this paper, we introduce three different methods namely 3-dimensional (3-D) CNN, CNN with convolutional long short-term memory (ConvLSTM) layers and vision transformer that use temporal information along with spatial information for plant accession classification. We have used publicly available Arabidopsis plant accession classification dataset to test the performance of these methods. Time series color images of four different plant accessions of Arabidopsis are given as input to the neural network model which in turn identifies plant accession. All the three methods outperform the existing methods available in the literature in terms of average accession classification accuracy. Vision transformer achieves highest classification accuracy of 98.59% at the cost of very large number of trainable parameters as compared to the other two methods. On the other hand, CNN-ConvLSTM achieves comparable accuracy of 97.97% with very less trainable parameters as compared to vision transformer. In future, these models can also be used to identify unknown plant accessions and predict plant growth signatures in different climatic conditions.

最近，基于图像的植物表型被广泛用于植物性状估计、植物种质分类和选择以及植物胁迫分析。植物种质分类和选择有助于识别对当地气候条件具有耐受性的植物。在过去几年中，卷积神经网络 (CNN) 模型主要用于使用空间特征从静态植物图像中对植物和植物病害进行分类。在本文中，我们介绍了三种不同的方法，即 3 维 (3-D) CNN、具有卷积长短期记忆 (ConvLSTM) 层的 CNN 和使用时间信息和空间信息进行植物分类的视觉变换器。我们使用公开可用的拟南芥植物种质分类数据集来测试这些方法的性能。拟南芥四种不同植物种质的时间序列彩色图像作为神经网络模型的输入，进而识别植物种质。在平均种质分类精度方面，这三种方法都优于文献中可用的现有方法。与其他两种方法相比，Vision Transformer 以非常大量的可训练参数为代价实现了 98.59% 的最高分类准确率。另一方面，与视觉变换器相比，CNN-ConvLSTM 实现了 97.97% 的可比精度，可训练参数非常少。将来，这些模型还可用于识别未知植物种质并预测不同气候条件下的植物生长特征。