Deep Convolutional Neural Networks Based Framework for Estimation of Stomata Density and Structure from Microscopic Images

  • Swati BhugraEmail author
  • Deepak MishraEmail author
  • Anupama AnupamaEmail author
  • Santanu ChaudhuryEmail author
  • Brejesh LallEmail author
  • Archana ChughEmail author
  • Viswanathan ChinnusamyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


Analysis of stomata density and its configuration based on scanning electron microscopic (SEM) image of a leaf surface, is an effective way to characterize the plant’s behaviour under various environmental stresses (drought, salinity etc.). Existing methods for phenotyping these stomatal traits are often based on manual or semi-automatic labeling and segmentation of SEM images. This is a low-throughput process when large number of SEM images is investigated for statistical analysis. To overcome this limitation, we propose a novel automated pipeline leveraging deep convolutional neural networks for stomata detection and its quantification. The proposed framework shows a superior performance in contrast to the existing stomata detection methods in terms of precision and recall, 0.91 and 0.89 respectively. Furthermore, the morphological traits (i.e. length & width) obtained at stomata quantification step shows a correlation of 0.95 and 0.91 with manually computed traits, resulting in an efficient and high-throughput solution for stomata phenotyping.


High-throughput phenotyping Deep convolutional neural networks Stomata counting Stomata quantification 



This work is supported by National Agricultural Science Fund (NASF) under Indian Council of Agricultural Research (ICAR), Delhi, India [Phenomics of moisture deficit stress tolerance and nitrogen use efficiency in rice and wheat- Phase II]. The authors are thankful to the Department of Textile Technology, Indian Institute of Technology Delhi (IIT Delhi) for the SEM facility.

Supplementary material

478828_1_En_31_MOESM1_ESM.pdf (1.7 mb)
Supplementary material 1 (pdf 1731 KB)


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology DelhiNew DelhiIndia
  2. 2.Indian Agricultural Research InstituteNew DelhiIndia

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