scholarly journals An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis

PLoS ONE ◽  
2018 ◽  
Vol 13 (4) ◽  
pp. e0196615 ◽  
Author(s):  
Unseok Lee ◽  
Sungyul Chang ◽  
Gian Anantrio Putra ◽  
Hyoungseok Kim ◽  
Dong Hwan Kim
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
Plant Phenotyping

scholarly journals Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

scholarly journals HTPheno: An image analysis pipeline for high-throughput plant phenotyping

2011 ◽  
Vol 12 (1) ◽  
pp. 148 ◽  
Author(s):  
Anja Hartmann ◽  
Tobias Czauderna ◽  
Roberto Hoffmann ◽  
Nils Stein ◽  
Falk Schreiber
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Analysis Pipeline

scholarly journals Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning

2018 ◽  
Vol 315 (6) ◽  
pp. F1644-F1651 ◽  
Author(s):  
Susan M. Sheehan ◽  
Ron Korstanje
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Statistical Analysis ◽  
High Throughput ◽  
Quantitative Data ◽  
Human Interaction ◽  
Physiological Data ◽  
Data Sets ◽  
Quantitative Measurements ◽  
Consistent Manner

Current methods of scoring histological kidney samples, specifically glomeruli, do not allow for collection of quantitative data in a high-throughput and consistent manner. Neither untrained individuals nor computers are presently capable of identifying glomerular features, so expert pathologists must do the identification and score using a categorical matrix, complicating statistical analysis. Critical information regarding overall health and physiology is encoded in these samples. Rapid comprehensive histological scoring could be used, in combination with other physiological measures, to significantly advance renal research. Therefore, we used machine learning to develop a high-throughput method to automatically identify and collect quantitative data from glomeruli. Our method requires minimal human interaction between steps and provides quantifiable data independent of user bias. The method uses free existing software and is usable without extensive image analysis training. Validation of the classifier and feature scores in mice is highlighted in this work and shows the power of applying this method in murine research. Preliminary results indicate that the method can be applied to data sets from different species after training on relevant data, allowing for fast glomerular identification and quantitative measurements of glomerular features. Validation of the classifier and feature scores are highlighted in this work and show the power of applying this method. The resulting data are free from user bias. Continuous data, such that statistical analysis can be performed, allows for more precise and comprehensive interrogation of samples. These data can then be combined with other physiological data to broaden our overall understanding of renal function.

scholarly journals Leveraging Image Analysis for High-Throughput Plant Phenotyping

2019 ◽  
Vol 10 ◽  
Author(s):  
Sruti Das Choudhury ◽  
Ashok Samal ◽  
Tala Awada
Keyword(s):  
Image Analysis ◽  
High Throughput ◽  
Plant Phenotyping

scholarly journals DeepCob: Precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics

2021 ◽  
Author(s):  
Lydia Kienbaum ◽  
Miguel Correa Abondano ◽  
Raul H. Blas Sevillano ◽  
Karl J Schmid
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Deep Learning ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Learning Methods ◽  
Maize Cob ◽  
Classical Image ◽  
Maize Cobs

Background: Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNN) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results: Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy (r=0.99). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average RGB values for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10-20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3,449 images of 2,484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions: Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.

scholarly journals DeepCob: precise and high-throughput analysis of maize cob geometry using deep learning with an application in genebank phenomics

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Lydia Kienbaum ◽  
Miguel Correa Abondano ◽  
Raul Blas ◽  
Karl Schmid
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Deep Learning ◽  
High Throughput ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Learning Methods ◽  
Maize Cob ◽  
Classical Image ◽  
Maize Cobs

Abstract Background Maize cobs are an important component of crop yield that exhibit a high diversity in size, shape and color in native landraces and modern varieties. Various phenotyping approaches were developed to measure maize cob parameters in a high throughput fashion. More recently, deep learning methods like convolutional neural networks (CNNs) became available and were shown to be highly useful for high-throughput plant phenotyping. We aimed at comparing classical image segmentation with deep learning methods for maize cob image segmentation and phenotyping using a large image dataset of native maize landrace diversity from Peru. Results Comparison of three image analysis methods showed that a Mask R-CNN trained on a diverse set of maize cob images was highly superior to classical image analysis using the Felzenszwalb-Huttenlocher algorithm and a Window-based CNN due to its robustness to image quality and object segmentation accuracy ($$r=0.99$$ r = 0.99 ). We integrated Mask R-CNN into a high-throughput pipeline to segment both maize cobs and rulers in images and perform an automated quantitative analysis of eight phenotypic traits, including diameter, length, ellipticity, asymmetry, aspect ratio and average values of red, green and blue color channels for cob color. Statistical analysis identified key training parameters for efficient iterative model updating. We also show that a small number of 10–20 images is sufficient to update the initial Mask R-CNN model to process new types of cob images. To demonstrate an application of the pipeline we analyzed phenotypic variation in 19,867 maize cobs extracted from 3449 images of 2484 accessions from the maize genebank of Peru to identify phenotypically homogeneous and heterogeneous genebank accessions using multivariate clustering. Conclusions Single Mask R-CNN model and associated analysis pipeline are widely applicable tools for maize cob phenotyping in contexts like genebank phenomics or plant breeding.

Sign in / Sign up

Export Citation Format

Share Document