scholarly journals A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ronghao Wang ◽  
Yumou Qiu ◽  
Yuzhen Zhou ◽  
Zhikai Liang ◽  
James C. Schnable
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Plant Growth ◽  
High Throughput ◽  
Confidence Region ◽  
Morphological Operations ◽  
Growth Curve Analysis ◽  
High Throughput Phenotyping

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

scholarly journals Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture

Plant Methods ◽  
2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Abelardo Montesinos-López ◽  
Osval A. Montesinos-López ◽  
Gustavo de los Campos ◽  
José Crossa ◽  
Juan Burgueño ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
High Throughput ◽  
Functional Regression ◽  
Modern Agriculture ◽  
High Throughput Phenotyping

Development of an Automated High- Throughput Phenotyping System for Wheat Evaluation in a Controlled Environment

2019 ◽  
Vol 62 (1) ◽  
pp. 61-74 ◽  
Author(s):  
Chongyuan Zhang ◽  
Chongyuan Zhang ◽  
Michael O. Pumphrey ◽  
Jianfeng Zhou ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Image Processing ◽  
Heat Stress ◽  
Plant Breeding ◽  
Stripe Rust ◽  
High Throughput ◽  
Controlled Environment ◽  
High Throughput Phenotyping ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Wheat Lines

Abstract. Plant breeding has significantly improved in recent years; however, phenotyping remains a bottleneck, as the process of evaluating and measuring plant traits is often expensive, subjective, and laborious. Although commercial phenotyping systems are available, factors like cost, space, and need for specific controlled-environment conditions limit the affordability of these products. An accurate, user-friendly, adaptive, and high-throughput phenotyping (HTP) system is highly desirable to plant breeders, physiologists, and agronomists. To solve this problem, an automated HTP system and image processing algorithms were developed and tested in this study. The automated platform was an integration of an aluminum framework (including movement and control components), three cameras, and a laptop computer. A control program was developed using LabVIEW to manage operation of the system frame and sensors as a single-unit automated HTP system. Image processing algorithms were developed in MATLAB for high-throughput analysis of images acquired by the system to estimate phenotypes and traits associated with tested plants. The phenotypes extracted were color/spectral, texture, temperature, morphology, and greenness features on a temporal scale. Using two wheat lines with known heat tolerance, the functions of the HTP system were validated. Heat stress tolerance experiments revealed that features such as green leaf area and green normalized difference vegetation index derived from our system showed differences between the control and heat stress treatments, as well as between heat-tolerant and susceptible wheat lines. In another experiment, stripe rust resistance in wheat was assessed. With the HTP system, some potential for detecting qualitative traits, such as disease resistance, was observed, although further validation is needed. In summary, successful development and implementation of an automated system with custom image processing algorithms for HTP in wheat was achieved. Improvement of such systems would further help plant breeders, physiologists, and agronomists to phenotype crops in an efficient, objective, and high-throughput manner. Keywords: Automation, Heat stress, Image processing, Plant breeding, Sensing, Stripe rust.

scholarly journals Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

2015 ◽  
Vol 22 (5) ◽  
pp. 993-1000 ◽  
Author(s):  
Sheng Yu ◽  
Katherine P Liao ◽  
Stanley Y Shaw ◽  
Vivian S Gainer ◽  
Susanne E Churchill ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Feature Extraction ◽  
High Throughput ◽  
Knowledge Sources ◽  
Phenotype Classification ◽  
Feature Extraction And Selection ◽  
Automated Feature Extraction ◽  
High Throughput Phenotyping ◽  
Text Features ◽  
Selection Of

Abstract Objective Analysis of narrative (text) data from electronic health records (EHRs) can improve population-scale phenotyping for clinical and genetic research. Currently, selection of text features for phenotyping algorithms is slow and laborious, requiring extensive and iterative involvement by domain experts. This paper introduces a method to develop phenotyping algorithms in an unbiased manner by automatically extracting and selecting informative features, which can be comparable to expert-curated ones in classification accuracy. Materials and methods Comprehensive medical concepts were collected from publicly available knowledge sources in an automated, unbiased fashion. Natural language processing (NLP) revealed the occurrence patterns of these concepts in EHR narrative notes, which enabled selection of informative features for phenotype classification. When combined with additional codified features, a penalized logistic regression model was trained to classify the target phenotype. Results The authors applied our method to develop algorithms to identify patients with rheumatoid arthritis and coronary artery disease cases among those with rheumatoid arthritis from a large multi-institutional EHR. The area under the receiver operating characteristic curves (AUC) for classifying RA and CAD using models trained with automated features were 0.951 and 0.929, respectively, compared to the AUCs of 0.938 and 0.929 by models trained with expert-curated features. Discussion Models trained with NLP text features selected through an unbiased, automated procedure achieved comparable or slightly higher accuracy than those trained with expert-curated features. The majority of the selected model features were interpretable. Conclusion The proposed automated feature extraction method, generating highly accurate phenotyping algorithms with improved efficiency, is a significant step toward high-throughput phenotyping.

scholarly journals Surrogate-assisted feature extraction for high-throughput phenotyping

2016 ◽  
Vol 24 (e1) ◽  
pp. e143-e149 ◽  
Author(s):  
Sheng Yu ◽  
Abhishek Chakrabortty ◽  
Katherine P Liao ◽  
Tianrun Cai ◽  
Ashwin N Ananthakrishnan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Language Processing ◽  
High Throughput ◽  
Gold Standard ◽  
Characteristic Curve ◽  
Feature Selection Method ◽  
International Classification Of Diseases ◽  
Silver Standard ◽  
Automated Feature Extraction ◽  
High Throughput Phenotyping

Objective: Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy. Methods: The proposed Surrogate-Assisted Feature Extraction (SAFE) method selects candidate features from a pool of comprehensive medical concepts found in publicly available knowledge sources. The target phenotype’s International Classification of Diseases, Ninth Revision and natural language processing counts, acting as noisy surrogates to the gold-standard labels, are used to create silver-standard labels. Candidate features highly predictive of the silver-standard labels are selected as the final features. Results: Algorithms were trained to identify patients with coronary artery disease, rheumatoid arthritis, Crohn’s disease, and ulcerative colitis using various numbers of labels to compare the performance of features selected by SAFE, a previously published automated feature extraction for phenotyping procedure, and domain experts. The out-of-sample area under the receiver operating characteristic curve and F-score from SAFE algorithms were remarkably higher than those from the other two, especially at small label sizes. Conclusion: SAFE advances high-throughput phenotyping methods by automatically selecting a succinct set of informative features for algorithm training, which in turn reduces overfitting and the needed number of gold-standard labels. SAFE also potentially identifies important features missed by automated feature extraction for phenotyping or experts.

scholarly journals Non-destructive measurement for estimating leaf area of Bellis perennis

2017 ◽  
Vol 10 (1) ◽  
pp. 58-64
Author(s):  
Indera Sakti Nasution
Keyword(s):  
Image Processing ◽  
Plant Growth ◽  
Leaf Area ◽  
Color Space ◽  
Morphological Operations ◽  
Connected Component ◽  
Kinect Camera ◽  
Growth Estimation ◽  
Destructive Measurement ◽  
Non Destructive

Non-destructive measurement of approaches of modeling can be very convenient and useful for plant growth estimation. This study, digital image processing was evaluated as a non-destructive technique to estimate leaf area of Bellis perennis. The plant samples were growing in the greenhouse and the images were taken every day using Kinect camera. The proposed method used combination of L*a*b* color space, Otsu’s thresholding, morphological operations and connected component analysis to estimate leaf area of Bellis perennis. L* channel was used to distinguish the leaves and background. Calibration area uses a pot of known area in each image as a scale to calibrate the leaves area. The results show that the algorithm is able to separate leaf pixels from soil or pot backgrounds, and also allow it to be implemented in greenhouse automatically. This algorithm can be used for other plants in assumption that there is not too much leaf overlapped during measurement.

scholarly journals Strong temporal dynamics of QTL action on plant growth progression revealed through high‐throughput phenotyping in canola

2019 ◽  
Vol 18 (1) ◽  
pp. 68-82 ◽  
Author(s):  
Dominic Knoch ◽  
Amine Abbadi ◽  
Fabian Grandke ◽  
Rhonda C. Meyer ◽  
Birgit Samans ◽  
...  
Keyword(s):  
Plant Growth ◽  
High Throughput ◽  
Temporal Dynamics ◽  
High Throughput Phenotyping
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