BACKGROUND High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addressed. Advances in sensor technology have led to the increasing use of imaging to monitor and measure a range of plants including the model Arabidopsis thaliana. These extensive datasets contain diverse trait information, but feature extraction is often still implemented using approaches requiring substantial manual input. RESULTS The computational detection and segmentation of individual fruits from images is a challenging task, for which we have developed DeepPod, a patch-based 2-phase deep learning framework. The associated manual annotation task is simple and cost-effective without the need for detailed segmentation or bounding boxes. Convolutional neural networks (CNNs) are used for classifying different parts of the plant inflorescence, including the tip, base, and body of the siliques and the stem inflorescence. In a post-processing step, different parts of the same silique are joined together for silique detection and localization, whilst taking into account possible overlapping among the siliques. The proposed framework is further validated on a separate test dataset of 2,408 images. Comparisons of the CNN-based prediction with manual counting (R2 = 0.90) showed the desired capability of methods for estimating silique number. CONCLUSIONS The DeepPod framework provides a rapid and accurate estimate of fruit number in a model system widely used by biologists to investigate many fundemental processes underlying growth and reproduction.

中文翻译：

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DeepPod：一种基于卷积神经网络的拟南芥果实数量定量分析。

背景技术基于无损成像的高通量表型在植物生物学和育种程序中具有巨大的潜力。然而，从图像数据进行有效的特征提取和量化仍然是需要解决的瓶颈。传感器技术的进步导致越来越多地使用成像技术来监测和测量一系列植物，包括拟南芥模型。这些广泛的数据集包含各种特征信息，但特征提取通常仍使用需要大量人工输入的方法来实现。结果从图像中对单个水果进行计算检测和分割是一项艰巨的任务，为此，我们开发了DeepPod（基于补丁的2阶段深度学习框架）。关联的手动注释任务既简单又经济高效，而无需详细的分割或边界框。卷积神经网络（CNN）用于对植物花序的不同部分进行分类，包括角果的尖端，基部和主体以及茎的花序。在后处理步骤中，将同一个筒仓的不同部分连接在一起，以进行筒仓检测和定位，同时要考虑各个筒仓之间可能存在的重叠。在2408张图像的单独测试数据集上进一步验证了所提出的框架。将基于CNN的预测与手动计数（R2 = 0.90）进行比较，显示了估算长角数的方法的所需功能。