scholarly journals High-throughput fabrication of right-angle prism mirrors with selective metalization by two-step 3D printing and computer vision alignment

2020 ◽  
Author(s):  
Andrea Bertoncini ◽  
Gheorghe Cojoc ◽  
Jochen Guck ◽  
Carlo Liberale
Keyword(s):  
Computer Vision ◽  
3D Printing ◽  
High Throughput ◽  
Vision Alignment

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 Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery

2021 ◽  
Vol 182 ◽  
pp. 106011
Author(s):  
Samiul Haque ◽  
Edgar Lobaton ◽  
Natalie Nelson ◽  
G. Craig Yencho ◽  
Kenneth V. Pecota ◽  
...  
Keyword(s):  
Computer Vision ◽  
High Throughput ◽  
Size And Shape ◽  
Horticultural Crops

scholarly journals Open source computer vision-based layer-wise 3D printing analysis

2020 ◽  
Vol 36 ◽  
pp. 101473 ◽  
Author(s):  
Aliaksei L. Petsiuk ◽  
Joshua M. Pearce
Keyword(s):  
Computer Vision ◽  
3D Printing ◽  
Open Source

scholarly journals Design and characterization of a 3D-printed staggered herringbone mixer

BioTechniques ◽  
2021 ◽  
Author(s):  
Vedika J Shenoy ◽  
Chelsea ER Edwards ◽  
Matthew E Helgeson ◽  
Megan T Valentine
Keyword(s):  
3D Printing ◽  
High Throughput ◽  
Low Cost ◽  
Microfluidic Devices ◽  
Device Design ◽  
Replica Molding ◽  
3D Printed ◽  
And Performance ◽  
To Receive

3D printing holds potential as a faster, cheaper alternative compared with traditional photolithography for the fabrication of microfluidic devices by replica molding. However, the influence of printing resolution and quality on device design and performance has yet to receive detailed study. Here, we investigate the use of 3D-printed molds to create staggered herringbone mixers (SHMs) with feature sizes ranging from ∼100 to 500 μm. We provide guidelines for printer calibration to ensure accurate printing at these length scales and quantify the impacts of print variability on SHM performance. We show that SHMs produced by 3D printing generate well-mixed output streams across devices with variable heights and defects, demonstrating that 3D printing is suitable and advantageous for low-cost, high-throughput SHM manufacturing.

scholarly journals High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Chase Correia ◽  
Seamus Mawe ◽  
Shane Lofgren ◽  
Roberta G. Marangoni ◽  
Jungwha Lee ◽  
...  
Keyword(s):  
Computer Vision ◽  
Systemic Sclerosis ◽  
High Throughput ◽  
Skin Disease ◽  
Quantitative Histology

Hacking blu-ray drives for high-throughput 3D printing

2021 ◽  
Author(s):  
Edwin En-Te Hwu ◽  
Martin Voss ◽  
Tien-Jen Chang ◽  
Hsien-Shun Liao ◽  
Anja Boisen
Keyword(s):  
3D Printing ◽  
High Throughput

scholarly journals 227 Leveraging on high-throughput phenotyping technologies to optimize livestock genetic improvement and husbandry

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 55-55
Author(s):  
Guilherme J M Rosa ◽  
João R R Dorea ◽  
Arthur Francisco Araujo Fernandes ◽  
Tiago L Passafaro
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
High Throughput ◽  
Feed Intake ◽  
Genetic Improvement ◽  
Milk Composition ◽  
Sensor Technology ◽  
Efficient Data ◽  
High Throughput Phenotyping ◽  
Complex Relationships

Abstract The advent of fully automated data recording technologies and high-throughput phenotyping (HTP) systems has opened up a myriad of opportunities to advance breeding programs and livestock husbandry. Such technologies allow scoring large number of animals for novel phenotypes and indicator traits to boost genetic improvement, as well as for real-time monitoring of animal behavior and development for optimized management decisions. HTP tools include, for example, image analysis and computer vision, sensor technology for motion, sound and chemical composition, and spectroscopy. Applications span from health surveillance, precision nutrition, and control of meat and milk composition and quality. However, the application of HTP requires sophisticated statistical and computational approaches for efficient data management and appropriate data mining, as it involves large datasets with many covariates and complex relationships. In this talk we will discuss some of the challenges and potentials of HTP in livestock. Some examples to be presented include the utilization of automated feeders to record feed intake and to monitor feeding behavior in broilers, milk-spectra information to predict dairy cattle feed intake, and image analysis and computer vision to monitor growth and body condition in pigs and cattle. HTP and big data will become an essential component of modern livestock operations in the context of precision animal agriculture, boosting animal welfare, environmental footprint, and overall sustainability of animal production.

scholarly journals High-Throughput Computer Vision Introduces the Time Axis to a Quantitative Trait Map of a Plant Growth Response

Genetics ◽  
2013 ◽  
Vol 195 (3) ◽  
pp. 1077-1086 ◽  
Author(s):  
Candace R. Moore ◽  
Logan S. Johnson ◽  
Il-Youp Kwak ◽  
Miron Livny ◽  
Karl W. Broman ◽  
...  
Keyword(s):  
Computer Vision ◽  
Plant Growth ◽  
High Throughput ◽  
Quantitative Trait ◽  
Growth Response ◽  
Time Axis ◽  
Plant Growth Response

scholarly journals Maize-IAS: A Maize Image Analysis Software using Deep Learning for High-throughput Plant Phenotyping

2021 ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Food Sources ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Phenotypic Traits ◽  
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-PAS ( Maize Phenotyping 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-PAS 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. Keywords : Maize phenotyping; Instance segmentation; Computer vision; Deep learning; Convolutional neural network

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