Image analysis software and sample preparation demands

1990 ◽  
Author(s):  
Karl n. Roth ◽  
Knut Wenzelides ◽  
Guenter Wolf ◽  
Peter Hufnagl
Keyword(s):  
Image Analysis ◽  
Sample Preparation ◽  
Analysis Software ◽  
Image Analysis Software

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.

Estimation of bodyweight from body measurements and determination of body measurements on Limousin cattle using digital image analysis

2016 ◽  
Vol 56 (12) ◽  
pp. 2060 ◽  
Author(s):  
Serkan Ozkaya ◽  
Wojciech Neja ◽  
Sylwia Krezel-Czopek ◽  
Adam Oler
Keyword(s):  
Image Analysis ◽  
Body Length ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Digital Camera ◽  
Body Measurements ◽  
Analysis Software ◽  
Body Area ◽  
Image Analysis Software

The objective of this study was to predict bodyweight and estimate body measurements of Limousin cattle using digital image analysis (DIA). Body measurements including body length, wither height, chest depth, and hip height of cattle were determined both manually (by measurements stick) and by using DIA. Body area was determined by using DIA. The images of Limousin cattle were taken while cattle were standing in a squeeze chute by a digital camera and analysed by image analysis software to obtain body measurements of each animal. While comparing the actual and predicted body measurements, the accuracy was determined as 98% for wither height, 97% for hip height, 94% for chest depth and 90.6% for body length. Regression analysis between body area and bodyweight yielded an equation with R2 of 61.5%. The regression equation, which included all body traits, resulted in an R2 value of 88.7%. The results indicated that DIA can be used for accurate prediction of body measurements and bodyweight of Limousin cattle.

Research on image analysis software for elevator brake wheel monitoring

2021 ◽  
Author(s):  
Shuangchang Feng ◽  
Pengzhao Zhang ◽  
Wenhao Shen ◽  
Pengbo Liu
Keyword(s):  
Image Analysis ◽  
Analysis Software ◽  
Image Analysis Software

scholarly journals Anatomical Characteristics of Garcinia lucida (Vesque) and Scorodophloeus zenkeri (Harms) Wood and Debarking Response in the South Region Cameroon

2017 ◽  
Vol 6 (4) ◽  
pp. 132
Author(s):  
Marie Caroline Momo Solefack ◽  
Hans Beeckman ◽  
Lucie Felicite Temgoua ◽  
Ghislain Kenguem Kinjouo
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Digital Image ◽  
Digital Image Analysis ◽  
Analysis Software ◽  
Anatomical Changes ◽  
Image Analysis Software ◽  
Conductive Zone ◽  
Before And After

The aim of this work was to investigate the possible anatomical changes of Garcinia lucida and Scorodophloeus zenkeri after the removal of their bark. Debarking was done on individuals of each species at 1.30 m from the soil. The wound was rectangular in shape with 30 cm side. There was a follow-up every three months for nine months during which the survival and rate of regeneration of the bark were recorded. A block of cube was cut from the regenerated and intact wood of species for microtomy and microscopy activities. On the cross-section of each wood, vessel features like density and diameter were measured before and after wounding. Semi-automatic measurements were made using the SpectrumSee digital image analysis software. In the wood of the two species, it appeared that the density of the vessels before debarking was significantly comparable to the density after debarking, while the diameter of vessels in the regenerated wood was smaller. The cambial area increased slightly in the rainy season for all species. After nine months all the species started the restoration of their conductive zone. G. lucida heals its wound more rapidly than S. zenkeri.

scholarly journals Morphology and Phenotype of Peripheral Erythrocytes of Fish: A Rapid Screening of Images by Using Software

2016 ◽  
Vol 54 ◽  
pp. 27-41
Author(s):  
Soumendra Nath Talapatra ◽  
Priyadarshini Mitra ◽  
Snehasikta Swarnakar
Keyword(s):  
Image Analysis ◽  
Mammalian Cells ◽  
Yeast Cells ◽  
Rapid Screening ◽  
Biological Study ◽  
Biological Research ◽  
Analysis Software ◽  
Image Analysis Software ◽  
Comparative Results ◽  
Cellular Phenotypes

Many information of biological study as stained cells analysis under microscope cannot be obtained rich information like detail morphology, shape, size, proper intensity etc. but image analysis software can easily be detected all these parameters within short duration. The cells types can be yeast cells to mammalian cells. An attempt has been made to detect cellular abnormalities from an image of metronidazole (MTZ) treated compared to control images of peripheral erythrocytes of fish by using non-commercial, open-source, CellProfiler (CP) image analysis software (Ver. 2.1.0). The comparative results were obtained after analysis the software. In conclusion, this image based screening of Giemsa stained fish erythrocytes can be a suitable tool in biological research for primary toxicity prediction at DNA level alongwith cellular phenotypes. Moreover, still suggestions are needed in relation to accuracy of present analysis for Giemsa stained fish erythrocytes because previous works have been carried out images of cells with fluorescence dye.

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