Monitoring the timing of seedling emergence and early development via high-throughput phenotyping with computer vision is a challenging topic of high interest in plant science. While most studies focus on the measurements of leaf area index or detection of specific events such as emergence, little attention has been put on the identification of kinetics of events of early seedling development on a seed to seed basis. Imaging systems screened the whole seedling growth process from the top view. Precise annotation of emergence out of the soil, cotyledon opening, and appearance of first leaf was conducted. This annotated data set served to train deep neural networks. Various strategies to incorporate in neural networks, the prior knowledge of the order of the developmental stages were investigated. Best results were obtained with a deep neural network followed with a long short term memory cell, which achieves more than 90% accuracy of correct detection. This work provides a full pipeline of image processing and machine learning to classify three stages of plant growth plus soil on the different accessions of two species of red clover and alfalfa but which could easily be extended to other crops and other stages of development.

中文翻译：

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基于深度学习的幼苗发育检测。

通过计算机视觉的高通量表型监测幼苗出苗和早期发育的时间是植物科学中备受关注的具有挑战性的课题。虽然大多数研究集中在叶面积指数的测量或特定事件（如出苗）的检测上，但很少关注在种子到种子的基础上识别早期幼苗发育事件的动力学。成像系统从顶视图筛选整个幼苗生长过程。对出土、子叶开口和第一片叶子的外观进行了精确注释。这个带注释的数据集用于训练深度神经网络。研究了纳入神经网络的各种策略，研究了发展阶段顺序的先验知识。最好的结果是通过一个深度神经网络和一个长短期记忆细胞获得的，它达到了 90% 以上的正确检测准确率。这项工作提供了完整的图像处理和机器学习管道，可以对两种红三叶草和紫花苜蓿的不同种质的植物生长和土壤的三个阶段进行分类，但可以很容易地扩展到其他作物和其他发育阶段。