scholarly journals Automatic Recognition of Tea Diseases Based on Deep Learning

2020 ◽  
Author(s):  
Jing Chen ◽  
Junying Jia
Keyword(s):  
Deep Learning ◽  
Automatic Recognition

scholarly journals Detection and annotation of plant organs from digitised herbarium scans using deep learning

2020 ◽  
Vol 8 ◽  
Author(s):  
Sohaib Younis ◽  
Marco Schmidt ◽  
Claus Weiland ◽  
Stefan Dressler ◽  
Bernhard Seeger ◽  
...  
Keyword(s):  
Deep Learning ◽  
Automatic Recognition ◽  
Herbarium Specimens ◽  
Plant Organs ◽  
Detection Model ◽  
Plant Organ ◽  
Large Numbers ◽  
Bounding Boxes ◽  
Advanced Computer ◽  
Extract Information

As herbarium specimens are increasingly becoming digitised and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition of these organs will help mobilise such information. In our study, we use deep learning to detect plant organs on digitised herbarium specimens with Faster R-CNN. For our experiment, we manually annotated hundreds of herbarium scans with thousands of bounding boxes for six types of plant organs and used them for training and evaluating the plant organ detection model. The model worked particularly well on leaves and stems, while flowers were also present in large numbers in the sheets, but were not equally well recognised.

scholarly journals Variation of female pronucleus reveal oocyte or embryo abnormality: An expert experience deep learning of non-dark box analysis

2021 ◽  
Author(s):  
Jingwei Yang ◽  
Yikang Wang ◽  
Chong Li ◽  
Wei Han ◽  
Weiwei Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Measurement Method ◽  
Genetic Test ◽  
Automatic Recognition ◽  
Exponential Fitting ◽  
Qualitative Detection ◽  
Female Pronucleus ◽  
Analysis Of The Images ◽  
Zygote Stage ◽  
Paradoxical Results

Background: Pronuclear assessment appears to have the ability to distinguish good and bad embryos in the zygote stage,but paradoxical results were obtained in clinical studies.This situation might be caused by the robust qualitative detection of the development of dynamic pronuclei. Here,we aim to establish a quantitative pronuclear measurement method by applying expert experience deep learning from large annotated datasets. Methods: Convinced handle-annotated 2PN images(13419) were used for deep learning then corresponded errors were recorded through handle check for subsequent parameters adjusting. We used 790 embryos with 52479 PN images from 155 patients for analysis the area of pronuclei and the preimplantation genetic test results.Establishment of the exponential fitting equation and the key coefficient β1 was extracted from the model for quantitative analysis for pronuclear(PN) annotation and automatic recognition. Findings: Based on the female original PN coefficient β1,the chromosome normal rate in the blastocyst with biggest PN area is much higher than that of the blastocyst with smallest PN area(58.06% vs.45.16%, OR=1.68[1.07-2.64];P=0.031).After adjusting coefficient β1 by the first three frames which high variance of outlier PN areas was removed, coefficient β1 at 12 hours and at 14 hours post-insemination,similar but stronger evidence was obtained. All these discrepancies resulted from the female propositus in the PGT(SR) subgroup and smaller chromosomal errors. Conclusion(s): The results suggest that detailed analysis of the images of embryos could improve our understanding of developmental biology. Funding: None

SU-D-201-05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework

2016 ◽  
Vol 43 (6Part3) ◽  
pp. 3334-3335 ◽  
Author(s):  
A Santhanam ◽  
Y Min ◽  
P Beron ◽  
N Agazaryan ◽  
P Kupelian ◽  
...  
Keyword(s):  
Patient Safety ◽  
Deep Learning ◽  
Automatic Recognition ◽  
Safety Hazards ◽  
Camera System ◽  
Learning Framework ◽  
3D Camera

A deep learning approach for automatic recognition of seismo-volcanic events at the Cotopaxi volcano

2021 ◽  
Vol 409 ◽  
pp. 107142
Author(s):  
Fernando Lara ◽  
Román Lara-Cueva ◽  
Julio C. Larco ◽  
Enrique V. Carrera ◽  
Rubén León
Keyword(s):  
Deep Learning ◽  
Automatic Recognition ◽  
Learning Approach ◽  
Volcanic Events ◽  
Cotopaxi Volcano
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