Proceedings ArticleDOI
Automatic Quantification of Stomata for High-Throughput Plant Phenotyping
Swati Bhugra,Deepak Mishra,Anupama Anupama,Santanu Chaudhury,Brejesh Lall,Archana Chugh +5 more
- pp 3904-3910
TLDR
A novel automated framework for stomata quantification is proposed based on a hybrid approach where the candidateStomata region is first detected by a convolutional neural network and the occlusion is dealt with an inpainting algorithm to solve the problem of shape, scale and Occlusion in an end-to-end manner.Abstract:
Stomatal morphology is a key phenotypic trait for plants' response analysis under various environmental stresses (e.g. drought, salinity etc.). Stomata exhibit diverse characteristics with respect to orientation, size, shape and varying degree of papillae occlusion. Thus, the biologists currently rely on manual or semi-automatic approaches to accurately compute its morphological traits based on scanning electron microscopic (SEM) images of leaf surface. In contrast to these subjective and low-throughput methods, we propose a novel automated framework for stomata quantification. It is realized based on a hybrid approach where the candidate stomata region is first detected by a convolutional neural network (CNN) and the occlusion is dealt with an inpainting algorithm. In addition, we propose stomata segmentation based quantification framework to solve the problem of shape, scale and occlusion in an end-to-end manner. The performance of the proposed automated frameworks is evaluated by comparing the derived traits with manually computed morphological traits of stomata. With no prior information about its size and location, the hybrid and end-to-end machine learning frameworks shows a correlation of 0.94 and 0.93, respectively on rice stomata images. Furthermore, they successfully enable wheat stomata quantification showing generalizability in terms of cultivars.read more
Citations
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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
TL;DR: 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.
Journal ArticleDOI
Morphological, transcriptomic and proteomic responses of contrasting rice genotypes towards drought stress
TL;DR: In this paper, morphological, physiological, biochemical and molecular variations between drought tolerant (PB6 and Moroberakan) and drought sensitive (Way Rarem) varieties have been evaluated, and notable differences have been observed in root morphology, root xylem number and area, stomata number, relative water content, proline content, protein and gene expression.
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.
Posted ContentDOI
SAI: Fast and automated quantification of stomatal parameters on microscope images
Na Sai,James Paul Bockman,Hao Chen,Nathan S. Watson-Haigh,Bo Xu,Xueying Feng,A. Piechatzek,Chunhua Shen,Matthew Gilliham +8 more
TL;DR: StomaAI (SAI) is introduced: a reliable and user-friendly tool that measures stomata of the model plant Arabidopsis and the crop plant barley via the application of deep computer vision and is capable of producing measurements consistent with human experts and successfully reproduced conclusions of published datasets.
Journal ArticleDOI
An automatic plant leaf stoma detection method based on YOLOv5
TL;DR: Zhang et al. as mentioned in this paper proposed an automatic detection method for leaf stomatal morphology analysis based on an attention mechanism and deep learning, which adds a coordinate attention mechanism to the YOLOV5 backbone part.
References
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