Zhu Chuancheng, Hu Yusong, Mao Hude, Li Shumin, Li Fangfang, Zhao Congyuan, Luo Lin, Liu Weizhen, Yuan Xiaohui
School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China.
State Key Laboratory of Crop Stress Biology for Arid Areas, College of Plant Protection, Northwest A&F University, Shaanxi, China.
Front Plant Sci. 2021 Sep 3;12:716784. doi: 10.3389/fpls.2021.716784. eCollection 2021.
The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset ( L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.
叶片的气孔指数是气孔数量与气孔和表皮细胞总数的比值。与气孔密度相比，气孔指数在环境条件和叶片年龄方面相对恒定，因此是给定基因型或物种的诊断特征。传统的评估方法包括人工计数显微照片中的气孔和表皮细胞数量，这既费力又耗时。虽然已经提出了几种气孔密度的自动测量算法，但目前还没有气孔指数测量流程。本研究的主要目的是开发一种自动气孔指数测量流程。所提出的方法采用带有卷积神经网络的更快区域（R-CNN）和U-Net以及图像处理技术来计数气孔和表皮细胞，随后计算气孔指数。为了提高标注速度，在每张显微照片中采用半自动策略进行表皮细胞注释。在小麦数据集（L.）的1000张叶片表皮显微图像上对该流程进行基准测试，气孔和表皮细胞的平均计数准确率分别达到98.03%和95.03%，气孔指数的最终测量准确率为95.35%。气孔、表皮细胞和气孔指数自动测量与人工测量之间的值分别为0.995、0.983和0.895。整个流程的平均运行时间（ART）每张显微照片可短至0.32秒。所提出的流程通过迁移学习在其他植物科上也实现了良好的可迁移性，在七个植物科中气孔和表皮细胞的平均计数准确率分别为94.36%和91.13%，气孔指数准确率为89.38%。该流程是一种用于气孔指数测量的自动、快速且准确的工具，能够实现高通量表型分析，并有助于植物生理学领域进一步了解气孔和表皮发育。据我们所知，这是首个基于深度学习的用于气孔指数评估的显微照片分析流程。
