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Image-based plant phenotyping with incremental learning and active contours

Massimo Minervini, Mohammed M. Abdelsamea, Sotirios A. Tsaftaris
2014 Ecological Informatics  
., Image-based plant phenotyping with incremental learning and active contours, Ecological Informatics (2013), http://dx.  ...  Current solutions for automated image-based plant phenotyping, rely either on semi-automated or manual analysis of the imaging data, or on expensive and proprietary software which accompanies costly hardware  ...  Pierdomenico Perata and his group from Scuola Superiore Sant'Anna, for providing us with plant samples and instructions on growth conditions of Arabidopsis. They also thank Dr.  ... 
doi:10.1016/j.ecoinf.2013.07.004 fatcat:ibhvjxakbvfrbh567lnbf4r7km

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
Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems.  ...  This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method.  ...  ; the 186 Rosette 186 Tracker algorithm [13], the probabilistic parametric active contours algorithm [14], and the Image-based 187 plant phenotyping with incremental learning and active contours [11  ... 
doi:10.1101/2020.05.17.100917 fatcat:b2txl47nkrebxiwrk53d4bjsoi

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  
DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets.  ...  Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems.  ...  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

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.  ...  However, most available active contour models lack adaptive initial contour and priori information of target region.  ...  Minervini [2] proposed an image-based plant phenotyping with incremental learning and active contours for accurate plant segmentation.  ... 
doi:10.1007/978-3-319-21963-9_5 fatcat:p546jly3arf6fdfm5eawjzp3be

The use of plant models in deep learning: an application to leaf counting in rosette plants

Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Ian Stavness
2018 Plant Methods  
Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task.  ...  This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high.  ...  Background Non-destructive, image-based plant phenotyping has emerged as an active area of research in recent years.  ... 
doi:10.1186/s13007-018-0273-z pmid:29375647 pmcid:PMC5773030 fatcat:tulfs3uyarg6tkgewcm3iydqsa

SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation

Shuai Li, Zhuangzhuang Yan, Yixin Guo, Xiaoyan Su, Yangyang Cao, Bofeng Jiang, Fei Yang, Zhanguo Zhang, Dawei Xin, Qingshan Chen, Rongsheng Zhu
2021 Crop Journal  
All authors agreed to be accountable for all aspects of their work to ensure that the questions related to the accuracy or integrity of any part is appropriately investigated and resolved, and approved  ...  Previously, phenotypic studies of soybean plants were simple and fragmented, whereas phenomics is based on high-dimensional phenotypic data [14] .  ...  This platform can be utilized from fields and laboratories with an active internet connection, using a smartphone or computer.  ... 
doi:10.1016/j.cj.2021.05.014 fatcat:qiwh46atvfcs3g3s52ryb56q2m

Editorial — Special issue on multimedia in ecology

Concetto Spampinato, Vasileios Mezaris, Benoit Huet, Jacco van Ossenbruggen
2014 Ecological Informatics  
attention to animal and plant identification and classification and pollution analysis.  ...  The recent advances in digital cameras and sensors, as well as in network bandwidth and information storage capacities, have revolutionized our ability to capture multimedia data (sounds, images, videos  ...  We would like to thank, first, the authors for their contribution to this special issue, then, all the reviewers for the effort and time spent to provide thorough reviews and valuable suggestions on the  ... 
doi:10.1016/j.ecoinf.2014.03.001 fatcat:zvfaqhm2hrehjci7jsemjgms7u

Leaf Segmentation and Classification with a Complicated Background Using Deep Learning

Kunlong Yang, Weizhen Zhong, Fengguo Li
2020 Agronomy  
It could provide a reference for the phenotype analysis and automatic classification of plants.  ...  In this paper, the segmentation and classification of leaf images with a complicated background using deep learning are studied.  ...  As can be seen from Table 2 , the image classification methods based on deep learning achieved good results in plant recognition with a complicated background.  ... 
doi:10.3390/agronomy10111721 fatcat:mmuj3phb6fenzntgk4uehxjpxe

A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation

Jonathon A. Gibbs, Lorna Mcausland, Carlos A. Robles-Zazueta, Erik H. Murchie, Alexandra J. Burgess
2021 Frontiers in Plant Science  
This advanced method provides a step toward reducing the bottleneck associated with plant phenotyping approaches and will provide a rapid method to assess gas fluxes in plants based on stomata morphometry  ...  The automated deep learning-based method gave estimates for gsmax within 3.8 and 1.9% of those values manually calculated from an expert for a wheat and poplar dataset, respectively.  ...  Matthew Reynolds and Dr. Gemma Molero (CIMMYT) for access to wheat plant material from the PS Tails panel.  ... 
doi:10.3389/fpls.2021.780180 pmid:34925424 pmcid:PMC8675901 fatcat:6apfrhlsj5afrmye5qky5suwse

Visual Growth Tracking for Automated Leaf Stage Monitoring Based on Image Sequence Analysis

Srinidhi Bashyam, Sruti Das Choudhury, Ashok Samal, Tala Awada
2021 Remote Sensing  
In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping  ...  The method accepts an image sequence of a plant as the input and automatically generates a leaf status report containing the phenotypes, which are crucial in the understanding of a plant's growth, i.e.  ...  Acknowledgments: The authors would like to thank the High-Throughput Plant Phenotyping Core Facility (Scanalyzer 3D, LemnaTec Gmbh, Aachen, Germany), located at the Nebraska Innovation Campus of the University  ... 
doi:10.3390/rs13050961 fatcat:dxj2dsira5dpzlm43h7uvmn7zi

Video Bioinformatics Methods for Analyzing Cell Dynamics: A Survey [chapter]

Nirmalya Ghosh
2015 Computational Biology  
segmentation to cellular feature extraction and selection, classification into different phenotypes, and exploration of hidden content-based patterns in bioimaging databases.  ...  Computational tools from established fields like computer vision, pattern recognition, and machine learning have immensely improved quantification at different stages-from image preprocessing and cell  ...  They also address another edge-based segmentation operating on nonmaximum suppression algorithm and refining the contour by active contours (snakes) with energy function associated with curves.  ... 
doi:10.1007/978-3-319-23724-4_2 fatcat:wjsaagwnpbgmziy662tsmw6hv4

Automated Extraction of Phenotypic Leaf Traits of Individual Intact Herbarium Leaves from Herbarium Specimen Images Using Deep Learning Based Semantic Segmentation

Burhan Rashid Hussein, Owais Ahmed Malik, Wee-Hong Ong, Johan Willem Frederik Slik
2021 Sensors  
analysis, and a single-leaf classifier trained on binary images to automate the extraction of an intact individual leaf with phenotypic traits.  ...  Even for the plant part itself, a combination of different overlapping, damaged, and intact individual leaves exist together with other plant organs such as stems and fruits, which increases the complexity  ...  Acknowledgments: We gratefully acknowledge the Institute of Applied Data Analytics, Universiti Brunei Darussalam for providing the facilities and support.  ... 
doi:10.3390/s21134549 doaj:9a6569c86ca54ad093f0c3647d33c63c fatcat:s5nlkf3tlvhqbglmvliecbtynu

Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation [article]

M. Abdelsamea
2015 arXiv   pre-print
Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution.  ...  Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour  ...  Tsaftaris, Imagebased plant phenotyping with incremental learning and active contours, Ecological Informatics, vol. 23, pp. 35-48. 2014, Special Issue on Multimedia in Ecology and Environment. 4. M.  ... 
arXiv:1511.00111v1 fatcat:lba7gzjkivdn5a5hueow33t3bi

Arecanut Bunch Segmentation Using Deep Learning Techniques

Anitha A. C., R. , Dhanesha, Shrinivasa Naika C. L., Krishna A. N., Parinith S. Kumar, Parikshith P. Sharma
2022 North atlantic university union: International Journal of Circuits, Systems and Signal Processing  
This paper presents two deep-learning approaches: Mask Region-Based Convolutional Neural Network (Mask R-CNN) and U-Net for the segmentation of arecanut bunches from the tree images without any pre-processing  ...  A very few attempts been made for the segmentation of arecanut bunch and are based on hand-crafted features with limited performance.  ...  Acknowledgements The authors would like to thank R Dhanesha and Shrinivasa Naika C L [28] for making available the data set for sharing their data set.  ... 
doi:10.46300/9106.2022.16.129 fatcat:i347lhkx4bgvlkprefdluf2x7e

DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks

Pouria Sadeghi-Tehran, Nicolas Virlet, Eva M. Ampe, Piet Reyns, Malcolm J. Hawkesford
2019 Frontiers in Plant Science  
The proposed approach is validated with image-based ear counting and ground-based measurements.  ...  As the method is based on a deep learning model, it replaces hand-engineered features required for traditional machine learning methods with more efficient algorithms.  ...  ACKNOWLEDGMENTS Authors would like to thank Andrew Riche, David Steele, and March Castle for collecting images from the WGIN trial.  ... 
doi:10.3389/fpls.2019.01176 pmid:31616456 pmcid:PMC6775245 fatcat:7o7faggnbbcwnoy3uichghcocy
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