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3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants [chapter]

Jean-Michel Pape, Christian Klukas
2015 Lecture Notes in Computer Science  
Here we present a 3-D histogram-based segmentation and recognition approach for top view images of rosette plants such as Arabidopsis thaliana and tobacco.  ...  Furthermore a Euclidean-distance-map-based method for the detection of leaves and the corresponding plant leaf segmentation was developed.  ...  In the last step the region growing algorithm labels each leaf region. Pipeline Parameters Besides the trained 3-D histogram cubes several parameters influence the segmentation and leaf detection.  ... 
doi:10.1007/978-3-319-16220-1_5 fatcat:avwdemrnlbbntodmfmqba33wzq

Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects

J. De Vylder, F. Vandenbussche, Y. Hu, W. Philips, D. Van Der Straeten
2012 Plant Physiology  
In contrast with previously described monitoring tools, Rosette Tracker allows us to simultaneously quantify plant growth, photosynthesis, and leaf temperature-related parameters through the analysis of  ...  We introduce Rosette Tracker, a new open source image analysis tool for evaluation of plant-shoot phenotypes.  ...  ACKNOWLEDGMENTS The authors wish to thank Pieter Callebert, Laury Chaerle, and Xavier Vanrobaeys for setup and image acquisition of the time-lapse sequence in Example 2.  ... 
doi:10.1104/pp.112.202762 pmid:22942389 pmcid:PMC3490612 fatcat:n7sskorbljae7cdrljut5kdxoe

Developmental normalization of phenomics data generated by high throughput plant phenotyping systems

Diego Lozano-Claros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan, Mathew G. Lewsey
2020 Plant Methods  
The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number.  ...  DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets.  ...  Acknowledgements We thank Andrew Robinson for helping set up the infrastructure for data storage and transfer. We thank Dr. Ricarda Jost and Dr. Meiyan Ren for assisting with experiments.  ... 
doi:10.1186/s13007-020-00653-x pmid:32817754 pmcid:PMC7424680 fatcat:coxifch5kbhpdkfzz27yyq7yfy

Developmental Normalization of Phenomics Data Generated by High Throughput Plant Phenotyping Systems [article]

Diego Lozano-Claros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan, Mathew G Lewsey
2020 bioRxiv   pre-print
The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number.  ...  Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems.  ...  These stages were identified by the leaf number based on the adjusted BBCH scale [3]. number trend may vary from plant to plant within the same line/mutant population at a time point.  ... 
doi:10.1101/2020.05.17.100917 fatcat:b2txl47nkrebxiwrk53d4bjsoi

Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area

Nan An, Christine M. Palmer, Robert L. Baker, R.J. Cody Markelz, James Ta, Michael F. Covington, Julin N. Maloof, Stephen M. Welch, Cynthia Weinig
2016 Computers and Electronics in Agriculture  
16 Plant phenotyping is central to understand causal effects of genotypes and environments 17 on trait expression and is a critical factor in expediting plant breeding.  ...  In recent years, photogrammetry and image processing techniques have been introduced to 21 plant phenotyping, but cost efficiency issues remain when combining these two techniques 22  ...  Figure A) to 778 D) stands for Day 1 to Day 4, respectively. The green histograms are the distributions for 779 parameter a and the blue histograms are for parameter b.  ... 
doi:10.1016/j.compag.2016.04.002 fatcat:2vmv3qo4czdrvf5ex2mzugbyay

Antiviral ARGONAUTEs Against Turnip Crinkle Virus Revealed by Image-based Trait Analysis

Xingguo Zheng, Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry, James C. Carrington
2019 Plant Physiology  
This process captured and analyzed growth and leaf color of Arabidopsis (Arabidopsis thaliana) plants in response to virus infection over time.  ...  A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green, and blue imaging and a computational segmentation algorithm, was  ...  Kira Veley for support and critique of the study. We also thank Robyn Allscheid for constructive editorial advices on this manuscript.  ... 
doi:10.1104/pp.19.00121 pmid:31043494 pmcid:PMC6752898 fatcat:tadll5r4zbfqpeighdtu27es2a

Leveraging multiple datasets for deep leaf counting [article]

Andrei Dobrescu, Mario Valerio Giuffrida, Sotirios A Tsaftaris
2017 arXiv   pre-print
Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants.  ...  While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation  ...  We acknowledge and thank NVIDIA for providing hardware essential for our work. Finally we would like to thank all the organisers of the CVPPP workshop for making it happen.  ... 
arXiv:1709.01472v1 fatcat:3a23hdso5bfvlppkblf3n3mnra

Counting Dense Leaves under Natural Environments via anImproved Deep-Learning-Based Object Detection Algorithm

Shenglian Lu, Zhen Song, Wenkang Chen, Tingting Qian, Yingyu Zhang, Ming Chen, Guo Li
2021 Agriculture  
The leaf is the organ that is crucial for photosynthesis and the production of nutrients in plants; as such, the number of leaves is one of the key indicators with which to describe the development and  ...  To address the challenge in counting dense and overlapped plant leaves under natural environments, we proposed an improved deep-learning-based object detection algorithm by merging a space-to-depth module  ...  Aich and Stavness [24] used deep convolutional and deconvolutional network (DCDN) to segment the rosette plant region and to count the rosette plant leaves. Giuffrida et al.  ... 
doi:10.3390/agriculture11101003 fatcat:ubny6uzznvhclpywascmumhwoq

Leveraging multiple datasets for deep leaf counting [article]

Andrei Dobrescu, Mario Valerio Giuffrida, Sotirios A. Tsaftaris
2017 bioRxiv   pre-print
Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants.  ...  While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation  ...  We acknowledge and thank NVIDIA for providing hardware essential for our work. Finally we would like to thank all the organisers of the CVPPP workshop for making it happen.  ... 
doi:10.1101/185173 fatcat:modrbsebzbh2xanomyaauvarsq

Leveraging Multiple Datasets for Deep Leaf Counting

Andrei Dobrescu, Mario Valerio Giuffrida, Sotirios A. Tsaftaris
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants.  ...  While stateof-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation  ...  We acknowledge and thank NVIDIA for providing hardware essential for our work. Finally we would like to thank all the organisers of the CVPPP workshop for making it happen.  ... 
doi:10.1109/iccvw.2017.243 dblp:conf/iccvw/DobrescuGT17 fatcat:b6j4r6itvvfzrnqoqyogjb2rv4

Antiviral Functions of ARGONAUTE Proteins During Turnip Crinkle Virus Infection Revealed by Image-based Trait Analysis in Arabidopsis [article]

Xingguo Zheng, James C. Carrington, Noah Fahlgren, Arash Abbasi, Jeffrey C. Berry
2018 bioRxiv   pre-print
This process captured and analyzed growth and leaf color of Arabidopsis plants in response to virus infection over time.  ...  A quantitative phenotyping protocol using an image-based color trait analysis pipeline on the PlantCV platform, with temporal red, green and blue (RGB) imaging and a computational segmentation algorithm  ...  (B) One example of original raw plant 905 image, segmented pseudo-color image, and overlaid image of Col-0 plant inoculated with TCV CPB Fig 3 . 3 TCV infection-caused temporal changes in rosette size  ... 
doi:10.1101/487322 fatcat:ff7lhf7zt5cmfpdtlk65nhdmxi

Contour-Based Plant Leaf Image Segmentation Using Visual Saliency [chapter]

Zhou Qiangqiang, Wang Zhicheng, Zhao Weidong, Chen Yufei
2015 Lecture Notes in Computer Science  
In this paper, we presented a new method that is based on active contours combined with saliency map for plant leaf segmentation.  ...  Segmentation based on active contour has been received widespread concerns recently for its good flexible performance.  ...  Fig. 1 . 1 Saliency detect algorithm Fig. 2 . 2 Spatial distance compute (Colorfigure online) Fig. 3 .Fig. 4 . 34 Image-based plant phenotyping detect (Color figure online) Diseased plant leaf detect  ... 
doi:10.1007/978-3-319-21963-9_5 fatcat:p546jly3arf6fdfm5eawjzp3be

Leaf segmentation in plant phenotyping: a collation study

Hanno Scharr, Massimo Minervini, Andrew P. French, Christian Klukas, David M. Kramer, Xiaoming Liu, Imanol Luengo, Jean-Michel Pape, Gerrit Polder, Danijela Vukadinovic, Xi Yin, Sotirios A. Tsaftaris
2015 Machine Vision and Applications  
Here we focus on the most common rosette model plants, Arabidopsis and young tobacco.  ...  Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images.  ...  Acknowledgments We would like to thank participants of the 2014 CVPPP workshop for comments and annotators that have contributed to this work.  ... 
doi:10.1007/s00138-015-0737-3 fatcat:c4dmf4exezgi3kiaxdipw3h7ai

Arabidopsis CYCD3 D-type cyclins link cell proliferation and endocycles and are rate-limiting for cytokinin responses

W. Dewitte, S. Scofield, A. A. Alcasabas, S. C. Maughan, M. Menges, N. Braun, C. Collins, J. Nieuwland, E. Prinsen, V. Sundaresan, J. A. H. Murray
2007 Proceedings of the National Academy of Sciences of the United States of America  
overall organ growth, as well as mediating cytokinin effects in apical growth and development. cell division ͉ cyclin D ͉ flowering time ͉ plant development Author contributions: W.D., S.S., M.M., and  ...  Current understanding of the integration of cell division and expansion in the development of plant lateral organs such as leaves is limited.  ...  We thank Susan Howroyd for technical help, the Swiss Centre for Functional Genomics (Zürich, Switzerland) for microarray analysis, and T. Jack  ... 
doi:10.1073/pnas.0704166104 pmid:17726100 pmcid:PMC1964848 fatcat:7pdhct4vybdhzjhatheftsvyl4

Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping

Suxing Liu, Lucia Acosta-Gamboa, Xiuzhen Huang, Argelia Lorence
2017 Journal of Imaging  
In order to improve the quality of the 3D models, we segmented the plant objects based on the PlantCV platform.  ...  We present an image-based 3D plant reconstruction system that can be achieved by using a single camera and a rotation stand.  ...  Figure 3 . 3 Pseudo code for the object segmentation based on the PlantCV (Plant Computer Vision) suite. are the input images and R(x,y) are the segmented result images. , and , are the values from HSV  ... 
doi:10.3390/jimaging3030039 fatcat:pg3lwiomdvfkbizdpboit2codu
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