scholarly journals Development of automatic counting system for urediospores of wheat stripe rust based on image processing

2017 ◽  
Vol 10 (5) ◽  
pp. 134-143 ◽  
Author(s):  
Li Xiaolong ◽  
◽  
Ma Zhanhong ◽  
Fernando Bienvenido ◽  
Qin Feng ◽  
...  
Keyword(s):  
Image Processing ◽  
Stripe Rust ◽  
Counting System ◽  
Wheat Stripe Rust ◽  
Automatic Counting

Efficient Rebar Count Scheme Based on Image Recognition in Mechanical Engineering

2013 ◽  
Vol 340 ◽  
pp. 805-808
Author(s):  
Yi Long Lei ◽  
Jiong Zhao Yang ◽  
Yu Huan Zhang
Keyword(s):  
Image Processing ◽  
Image Recognition ◽  
Enterprise Management ◽  
Labor Intensity ◽  
Root Number ◽  
Image Processing Algorithm ◽  
Counting System ◽  
Automatic Counting ◽  
Steel Bar ◽  
System Composition

Nowadays, along with the higher requirement of the customer and the standardization of enterprise management, the finished product of steel bar production must be standardization packaged by root number; management requirements of bar fixed bundle of sticks are more stringent. The artificial count is used into the most of the steel bar production recently. So there are many problems. Such as labor intensity, easy fatigue, less efficient, and error-prone. The image recognition technology for online automatic counting system for a given period of time. It also can improve the speed and make the product more accuracy. This paper mainly talks about the system composition and image processing algorithm.

Automatic Counting System for Nuclear Track Based on DSP and Mathematical Morphology Algorithm

2014 ◽  
Vol 610 ◽  
pp. 287-290
Author(s):  
Yi Pan ◽  
Liang Jun Liu
Keyword(s):  
Image Processing ◽  
Mathematical Morphology ◽  
Nuclear Technology ◽  
Counting System ◽  
Automatic Counting ◽  
Manual Counting ◽  
Important Position ◽  
Processing Techniques ◽  
Morphology Algorithm ◽  
Visual Error

Parameter measurement of the solid state nuclear track occupies an extremely important position in the field of nuclear technology while limitation of the traditional manual counting method is very large. In recent years, DSP and image processing techniques are increasingly applied in the field of nuclear technology. This paper describes an automatic counting system for nuclear track based on DSP image processing platform which uses DSP hardware platform and mathematical morphology algorithm. This system can effectively separate the track point from the background and remove noise, and also accurately count helping to reduce the visual error of manual counting.

Automatic mantispid egg detection and counting using image nature

2020 ◽  
Vol 34 (22n24) ◽  
pp. 2040138
Author(s):  
Pei-Ying Yang ◽  
Chin-Dar Tseng ◽  
Tai-Lin Huang ◽  
Chao-Hong Liu ◽  
I-Hsing Tsai ◽  
...  
Keyword(s):  
Image Processing ◽  
Background Subtraction ◽  
Counting System ◽  
Insect Eggs ◽  
Background Estimation ◽  
Morphological Image Processing ◽  
Automatic Counting ◽  
Manual Counting ◽  
Detection And Counting ◽  
Morphological Image

Mantispids are small brown bugs about 1.5 cm in length. Mantispid eggs are produced in large quantities, with about 1000 eggs per spawning, and are tiny and densely packed. Traditionally, mantispid eggs are counted manually. However, counting such a large quantity of eggs is difficult. To provide accurate data for researchers, we detail methods to accurately detect and count the number of mantispid eggs using image processing. The following methods were used to count the mantispid eggs: background estimation, morphological image processing, background subtraction, stretching, image thresholding, gray-level transformation, labeling and counting. The results of automated counting were compared with the results of manual counting. The segmentation results were verified, and the accuracy of the mantispid egg counts was determined to be 100%. This provides a useful resource for mantispid egg counting. The automatic counting system cannot only count mantispid eggs, it can also be used to count other similar insect eggs.

Characterization of Wheat Stripe Rust Resistance Genes in Shaanmai 139

2010 ◽  
Vol 36 (1) ◽  
pp. 109-114 ◽  
Author(s):  
Hong ZHANG ◽  
Zhi-Long REN ◽  
Yin-Gang HU ◽  
Chang-You WANG ◽  
Wan-Quan JI
Keyword(s):  
Stripe Rust ◽  
Resistance Genes ◽  
Rust Resistance ◽  
Stripe Rust Resistance ◽  
Wheat Stripe Rust ◽  
Rust Resistance Genes

Egg-Counting System Using Image Processing and a Website for Monitoring

2021 ◽  
Author(s):  
Isoon Kanjanasurat ◽  
Woranidtha Krungseanmuang ◽  
Vasutorn Chaowalittawin ◽  
Boonchana Purahong
Keyword(s):  
Image Processing ◽  
Counting System

scholarly journals The barley immune receptor Mla recognizes multiple pathogens and contributes to host range dynamics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jan Bettgenhaeuser ◽  
Inmaculada Hernández-Pinzón ◽  
Andrew M. Dawson ◽  
Matthew Gardiner ◽  
Phon Green ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Stripe Rust ◽  
Resistance Genes ◽  
Host Species ◽  
Plant Pathogens ◽  
Puccinia Striiformis ◽  
Species Specificity ◽  
Wheat Stripe Rust ◽  
Barley Powdery Mildew ◽  
Barley Stripe Rust

AbstractCrop losses caused by plant pathogens are a primary threat to stable food production. Stripe rust (Puccinia striiformis) is a fungal pathogen of cereal crops that causes significant, persistent yield loss. Stripe rust exhibits host species specificity, with lineages that have adapted to infect wheat and barley. While wheat stripe rust and barley stripe rust are commonly restricted to their corresponding hosts, the genes underlying this host specificity remain unknown. Here, we show that three resistance genes, Rps6, Rps7, and Rps8, contribute to immunity in barley to wheat stripe rust. Rps7 cosegregates with barley powdery mildew resistance at the Mla locus. Using transgenic complementation of different Mla alleles, we confirm allele-specific recognition of wheat stripe rust by Mla. Our results show that major resistance genes contribute to the host species specificity of wheat stripe rust on barley and that a shared genetic architecture underlies resistance to the adapted pathogen barley powdery mildew and non-adapted pathogen wheat stripe rust.

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