Book ChapterDOI
Deep Convolutional Neural Networks Based Framework for Estimation of Stomata Density and Structure from Microscopic Images
Swati Bhugra,Deepak Mishra,Anupama Anupama,Santanu Chaudhury,Brejesh Lall,Archana Chugh,Viswanathan Chinnusamy +6 more
- pp 412-423
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TLDR
A novel automated pipeline leveraging deep convolutional neural networks for stomata detection and its quantification shows a superior performance in contrast to the existing stomATA detection methods in terms of precision and recall.Abstract:
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.read more
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Journal ArticleDOI
LabelStoma: A tool for stomata detection based on the YOLO algorithm
Ángela Casado-García,Arantza del-Canto,Álvaro Sanz-Sáez,Usue Pérez-López,Amaia Bilbao-Kareaga,Felix B. Fritschi,Jon Miranda-Apodaca,Alberto Muñoz-Rueda,Anna Sillero-Martínez,Ander Yoldi-Achalandabaso,Maite Lacuesta,Jónathan Heras +11 more
TL;DR: The analysis of plant stomata of different species will be more reliable and comparable; the developed tools will help to advance the understanding of CO2 and H2O dynamics in plants, such as photosynthesis and transpiration, and ecosystems related processes.
Journal ArticleDOI
An Automatic Method for Stomatal Pore Detection and Measurement in Microscope Images of Plant Leaf Based on a Convolutional Neural Network Model
TL;DR: In this paper, a Mask R-CNN (region-based convolutional neural network) was used to segment stomatal regions in microscope images of leaves and obtain the contour coordinates of the pore regions by ellipse fitting.
Journal ArticleDOI
A generalised approach for high-throughput instance segmentation of stomata in microscope images
TL;DR: In this paper, a Mask R-CNN is applied to estimate individual stomata boundaries, and a statistical filter is implemented at the Mask-R-CNN output to reduce the number of false positive generated by the network.
Journal ArticleDOI
A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment.
Sumona Biswas,Shovan Barma +1 more
TL;DR: A new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework is presented.
Journal ArticleDOI
StomataScorer: a portable and high-throughput leaf stomata trait scorer combined with deep learning and an improved CV model.
Xiuying Liang,Xu Xichen,Zhiwei Wang,He Lei,Kaiqi Zhang,Bo Liang,Junli Ye,Jiawei Shi,Xi Wu,Mingqiu Dai,Wanneng Yang +10 more
TL;DR: In this paper, a new method for detecting stomata and extracting stomatal traits was proposed to automatically and non-destructively measure stomatatal traits automatically and nondestructively, and two portable microscopes with different resolutions (tipScope with a 40× lens attached to a smartphone and ProScope HR2 with a 400× lens) are used to acquire images of living stomas in maize leaves.
References
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Proceedings Article
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