scholarly journals Custom built scanner and simple image processing pipeline enables low-cost, high-throughput phenotyping of maize ears

2019 ◽  
Author(s):  
Cedar Warman ◽  
John E Fowler
Keyword(s):  
Image Processing ◽  
High Throughput ◽  
Low Cost ◽  
Digital Camera ◽  
Cost Effective ◽  
Source Image ◽  
Processing Pipeline ◽  
Effective Combination ◽  
Transmission Data ◽  
High Throughput Phenotyping

AbstractHigh-throughput phenotyping systems are becoming increasingly powerful, dramatically changing our ability to document, measure, and detect phenomena. Unfortunately, taking advantage of these trends can be difficult for scientists with few resources, particularly when studying nonstandard biological systems. Here, we describe a powerful, cost-effective combination of a custom-built imaging platform and open-source image processing pipeline. Our maize ear scanner was built with off-the-shelf parts for <$80. When combined with a cellphone or digital camera, videos of rotating maize ears were captured and digitally flattened into projections covering the entire surface of the ear. Segregating GFP and anthocyanin seed markers were clearly distinguishable in ear projections, allowing manual annotation using ImageJ. Using this method, statistically powerful transmission data can be collected for hundreds of maize ears, accelerating the phenotyping process.

scholarly journals HIGH THROUGHPUT PHENOTYPING OF PHYSIOLOGICAL GROWTH DYNAMICS FROM UAS-BASED 3D MODELING IN SOYBEAN

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 357-361 ◽  
Author(s):  
M. Herrero-Huerta ◽  
K. M. Rainey
Keyword(s):  
High Throughput ◽  
Digital Camera ◽  
Growth Dynamics ◽  
Cost Effective ◽  
Growing Season ◽  
3D Modelling ◽  
Crop Genetics ◽  
High Throughput Phenotyping ◽  
Research And Education ◽  
Selection Of

<p><strong>Abstract.</strong> Nowadays, an essential tool to improve the efficiency of crop genetics is automated, precise and cost-effective phenotyping of the plants. The aim of this study is to generate a methodology for high throughput phenotyping the physiological growth dynamics of soybeans by UAS-based 3D modelling. During the 2018 growing season, a soybean experiment was performed at the Agronomy Center for Research and Education (ACRE) in West-Lafayette (Indiana, USA). Periodic images were acquired by G9X Canon compact digital camera on board senseFly eBee. The study area is reconstructed in 3D by Image-based modelling. Algorithms and techniques were combined to analyse growth dynamics of the crop via height variations and to quantify biomass. Results provide practical information for the selection of phenotypes for breeding.</p>

scholarly journals A cost-effective maize ear phenotyping platform enables rapid categorization and quantification of kernels

2020 ◽  
Author(s):  
Cedar Warman ◽  
Christopher M. Sullivan ◽  
Justin Preece ◽  
Michaela E. Buchanan ◽  
Zuzana Vejlupkova ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Low Cost ◽  
Digital Camera ◽  
Cost Effective ◽  
Detection Model ◽  
Biological Phenomena ◽  
Effective Combination ◽  
Wide Range ◽  
Maize Ear

AbstractHigh-throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost-effective combination of a custom-built imaging platform and deep-learning-based computer vision pipeline. A minimal version of the maize ear scanner was built with low-cost and readily available parts. The scanner rotates a maize ear while a cellphone or digital camera captures a video of the surface of the ear. Videos are then digitally flattened into two-dimensional ear projections. Segregating GFP and anthocyanin kernel phenotype are clearly distinguishable in ear projections, and can be manually annotated using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390,000 kernels, identifying male-specific transmission defects across a wide range of GFP-marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme (VPE). We show that by using this system, the quantification of transmission data and other ear phenotypes can be accelerated and scaled to generate large datasets for robust analyses.One sentence summaryA maize ear phenotyping system built from commonly available parts creates images of the surface of ears and identifies kernel phenotypes with a deep-learning-based computer vision pipeline.

scholarly journals Raspberry Pi Powered Imaging for Plant Phenotyping

2017 ◽  
Author(s):  
Jose C. Tovar ◽  
J. Steen Hoyer ◽  
Andy Lin ◽  
Allison Tielking ◽  
Monica Tessman ◽  
...  
Keyword(s):  
Image Processing ◽  
Open Source ◽  
High Throughput ◽  
Plant Diversity ◽  
Low Cost ◽  
Image Acquisition ◽  
Image Data ◽  
Raspberry Pi ◽  
Source Image ◽  
Plant Image

ABSTRACTPremise of the study: Image-based phenomics is a powerful approach to capture and quantify plant diversity. However, commercial platforms that make consistent image acquisition easy are often cost-prohibitive. To make high-throughput phenotyping methods more accessible, low-cost microcomputers and cameras can be used to acquire plant image data.Methods and Results: We used low-cost Raspberry Pi computers and cameras to manage and capture plant image data. Detailed here are three different applications of Raspberry Pi controlled imaging platforms for seed and shoot imaging. Images obtained from each platform were suitable for extracting quantifiable plant traits (shape, area, height, color) en masse using open-source image processing software such as PlantCV.Conclusion: This protocol describes three low-cost platforms for image acquisition that are useful for quantifying plant diversity. When coupled with open-source image processing tools, these imaging platforms provide viable low-cost solutions for incorporating high-throughput phenomics into a wide range of research programs.

scholarly journals Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

2020 ◽  
Vol 12 (6) ◽  
pp. 998 ◽  
Author(s):  
GyuJin Jang ◽  
Jaeyoung Kim ◽  
Ju-Kyung Yu ◽  
Hak-Jin Kim ◽  
Yoonha Kim ◽  
...  
Keyword(s):  
Plant Breeding ◽  
Unmanned Aerial Vehicle ◽  
High Throughput ◽  
New Technologies ◽  
Cost Effective ◽  
New Wave ◽  
Breeding Programs ◽  
Modern Agriculture ◽  
Aerial Vehicle ◽  
High Throughput Phenotyping

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.

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.

A Note on Development of a Low-cost and High-throughput SSR-based Genotyping Method in Peanut (Arachis hypogaea L.)

2011 ◽  
Vol 38 (2) ◽  
pp. 122-127 ◽  
Author(s):  
Jake Fountain ◽  
Hongde Qin ◽  
Charles Chen ◽  
Phat Dang ◽  
Ming Li Wang ◽  
...  
Keyword(s):  
High Throughput ◽  
Silver Staining ◽  
Low Cost ◽  
Digital Camera ◽  
Polyacrylamide Gel ◽  
Cultivar Development ◽  
Arachis Hypogaea L ◽  
Data Points ◽  
Breeding Selection ◽  
The Cost

ABSTRACT Peanut cultivar development has been dominated by conventional breeding methods, which have and will continue to play an important role. Applications of marker-assisted selection (MAS) have been used in peanut breeding selection but the cost of genotyping is still a considerable factor. The objective of this study was to introduce a simple, low-cost, and high-throughput protocol for peanut community. The developed system was based on a smaller (10.5 cm in length) polyacrylamide gel size system to separate PCR amplified DNA fragments and silver staining to visualize the bands. This system is very easy to operate, having one electrophoresis unit holds two vertical 52-sample gels, and the cost for purchasing the unit is less than $200. For instance, the electrophoresis runs about 1 hr and 40 min at 180 V for 9% polyacrylamide gel to separate small to medium sized DNA bands (&lt; 500 bp) or 1 hr and 20 min at 160 V for 6% polyacrylamide gel preferably for larger band separation (≥ 500 bp), but the gel concentrations and running times could be adjusted according to the polymorphic banding patterns and sizes to mitigate the drawback of this system of small gel-size. The silver staining takes about 30 min. After staining, the gels are placed on a light-box for genotype scoring and then photographed using a digital camera. The cost per gel is estimated at $0.54 and the cost for silver staining is estimated at $0.37. Therefore, the cost could be as low as $0.018 per data point, excluding PCR reaction and DNA preparation cost. A scientist has the potential to generate over 1,200 data points per day. This method has been used in the construction of a peanut genetic linkage map and QTL studies in our laboratory in conjunction with other methods.

scholarly journals Attendance Management System using Google Facenet for Facial Recognition

2021 ◽  
Vol 9 (VI) ◽  
pp. 5029-5036
Author(s):  
Rakesh Duggempudi
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Open Source ◽  
Management System ◽  
Facial Recognition ◽  
Cost Effective ◽  
Source Image ◽  
Student Attendance ◽  
Manual Work ◽  
View Update

Attendance management system is a required tool for attaining attendance in any habitat where attendance is essential. Yet, many of the available techniques consume time, are invasive and it demands manual work from the users. This research is directed at building a less invasive, cost effective and more efficient automated student attendance management system using face recognition that leverages on OpenCV functions for facial recognition. The system provides a GUI for marking attendance. It provides an interface for updating attendance using facial recognition libraries of OpenCV. The system stores attendance in a database which is maintained by the administrator. The administrator can view, update, and change the attendance of the students. The students can view and update their attendance. The system is developed on Open-Source image processing library and the interface is developed using Python Tkinter module. The Tkinter module is an open-source module by which we can develop GUI screens hence, it is not software dependent nor vendor hardware. The OpenCV module used for image processing is interfaced using python.

scholarly journals Cost-effective, high-throughput phenotyping system for 3D reconstruction of fruit form

2021 ◽  
Author(s):  
Mitchell J Feldmann ◽  
Amy Tabb
Keyword(s):  
3D Imaging ◽  
Low Cost ◽  
Ground Truth ◽  
Cost Effective ◽  
Fruit Shape ◽  
3D Analysis ◽  
Image Capture ◽  
High Throughput Phenotyping ◽  
3D Printed ◽  
Specialized Hardware

Reliable phenotyping methods that are simple to operate and inexpensive to deploy are critical for studying quantitative traits in plants. Traditional fruit shape phenotyping relies on human raters or 2D analyses to assess form, e.g., size and shape. Systems for 3D imaging using multi-view stereo have been implemented, but frequently rely on commercial software and/or specialized hardware, which can lead to limitations in accessibility and scalability. We present a complete system constructed of consumer-grade components for capturing, calibrating, and reconstructing the 3D form of small-to-moderate sized fruits and tubers. Data acquisition and image capture sessions are 9 seconds to capture 60 images. The initial prototype cost was $1600 USD. We measured accuracy by comparing reconstructed models of 3D printed ground truth objects to the original digital files of those same ground truth objects. The R2 between length of the primary, secondary, and tertiary axes, volume, and surface area of the ground-truth object and the reconstructed models was > 0.97 and root-mean square error (RMSE) was <3mm for objects without locally concave regions. Measurements from 1mm and 2mm resolution reconstructions were consistent (R2 > 0.99). Qualitative assessments were performed on 48 fruit and tubers, including 18 strawberries, 12 potatoes, 5 grapes, 7 peppers, and 4 Bosch and 2 red Anjou pears. Our proposed phenotyping system is fast, relatively low cost, and has demonstrated accuracy for certain shape classes, and could be used for the 3D analysis of fruit form.

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