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Identifying barley varieties by computer vision

Piotr M. Szczypiński, Artur Klepaczko, Piotr Zapotoczny
2015 Computers and Electronics in Agriculture  
The development of automatic methods based on computer vision could have positive implications for the food processing industry.  ...  Visual discrimination between barley varieties is difficult, and it requires training and experience.  ...  The following computer programs were used in the study: Ziarna (https://gitorious.org/ziarna) for kernel image preprocessing and MaZda (https://gitorious.org/qmazda) for attribute computation and feature  ... 
doi:10.1016/j.compag.2014.09.016 fatcat:w2pcx753hzeefhc4fbxi2ljdh4

Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains

Piotr Boniecki, Agnieszka Sujak, Agnieszka A. Pilarska, Hanna Piekarska-Boniecka, Agnieszka Wawrzyniak, Barbara Raba
2022 Sensors  
malting barley.  ...  The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN).  ...  Computer vision supported by neural networks has been most frequently applied to identify varieties of different grains, including barley [19] [20] [21] [22] [23] 25, 54, 58] .  ... 
doi:10.3390/s22176578 pmid:36081052 pmcid:PMC9459746 fatcat:xaflg3cq7bfgvnxqdi5baj7rxm

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Jessica Fernandes Lopes, Leniza Ludwig, Douglas Fernandes Barbin, Maria Victória Eiras Grossmann, Sylvio Barbon
2019 Sensors  
Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods.  ...  The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification  ...  Differences in composition/physical characteristics between the two barley groups (from naked and malting barley) were detected by the computer vision system, and classification accuracy was improved using  ... 
doi:10.3390/s19132953 pmid:31277468 pmcid:PMC6650935 fatcat:nqhftdw55jbgrfcotbrfts7tc4

Computer vision algorithm for barley kernel identification, orientation estimation and surface structure assessment

Piotr M. Szczypiński, Piotr Zapotoczny
2012 Computers and Electronics in Agriculture  
The proposed algorithm was tested using barley grain images, and validated by comparison with the evaluation results of a professional assessor.  ...  Highlights An algorithm to analyze barley kernel images was proposed. The algorithm identified the wrinkled and smooth regions of individual kernels with a mean accuracy of 99%.  ...  Acknowledgment The author are grateful for the financial support provided by the Ministry of Scientific Research within the framework of Grant No. 4498/B/P01/2010/39.  ... 
doi:10.1016/j.compag.2012.05.014 fatcat:iiyacqm43zg6lmsgyj7lloqzxu

APPLICATION OF MACHINE VISION FOR DETECTION OF FOREIGN MATTER IN WHEAT GRAINS

G.K Arafa, Elbatawi I.E.
2008 Arab Universities Journal of Agricultural Sciences  
The color scheme could make a computer vision system very practical for foreign object detection and removal.  ...  On the other hand, 94%, 95% and 97% of 5% barley, rice and stones admixtures (with wheat) were accurately classified using neural network classifier.  ...  Color vision has also been used for meat and poultry inspection to identify meat quality and identify systemic defects in carcasses (Petracci et al 2004).  ... 
doi:10.21608/ajs.2008.14692 fatcat:jui2tg3f2bf2limsg3lnqo33ci

Barley Variety Recognition with Viewpoint-aware Double-stream Convolutional Neural Networks

Przemysław Dolata, Jacek Reiner
2018 Proceedings of the 2018 Federated Conference on Computer Science and Information Systems  
We show that it increases the average classification accuracy by 0.6% and sensitivity by 2.3% with respect to the viewpoint-ignorant architecture on our dataset.  ...  Varietal homogeneity is an important factor in quality of malting barley, but its inspection is difficult.  ...  In this study, we present a machine vision approach to recognition of barley varieties using convolutional neural networks.  ... 
doi:10.15439/2018f286 dblp:conf/fedcsis/DolataR18 fatcat:ocwmfj3kaffj5olyuuz76juape

Applications of Image Processing for Grading Agriculture products

Mayur P
2015 International Journal on Recent and Innovation Trends in Computing and Communication  
Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation.  ...  Computer vision is a novel technology for acquiring and analyzing an image of a real scene by computers to control machines or to process it.  ...  Computer Vision Computer vision is the transformation of data from a still or video camera into either a decision or a new representation.  ... 
doi:10.17762/ijritcc2321-8169.150367 fatcat:4tqk4cxzk5ad3imsfkip2fmoie

Quality Assessment of Components of Wheat Seed Using Different Classifications Models

Zargham Fazel-Niari, Amir H. Afkari-Sayyah, Yousef Abbaspour-Gilandeh, Israel Herrera-Miranda, José Luis Hernández-Hernández, Mario Hernández-Hernández
2022 Applied Sciences  
In the context of this study, the machine vision system—comprising an industrial digital camera and quadratic support vector machine or non-linear discriminate analysis method—was identified as a valuable  ...  The shape features were the most prominent, followed by the textural and colour features.  ...  The barley samples were prepared by mixing four local prevalent barley varieties (Bahman, Dasht, Khorram, and Makuyi).  ... 
doi:10.3390/app12094133 fatcat:s3oalfg5nzcpzkykm2ziqnccuy

Image Analysis Methods in Classifying Selected Malting Barley Varieties by Neural Modelling

Agnieszka A. Pilarska, Piotr Boniecki, Małgorzata Idzior-Haufa, Maciej Zaborowicz, Krzysztof Pilarski, Andrzej Przybylak, Hanna Piekarska-Boniecka
2021 Agriculture  
The project aims to elaborate on the original methodology used for identifying grain varieties, grain contamination degree and other visual characteristics of malting barley employing new computer technologies  ...  The neural modelling and digital image analysis assist in identifying the quality of barley varieties.  ...  In conclusion, colour turned out to be an identifying trait, represented by 15 characteristic parameters in classifying selected malting barley varieties.  ... 
doi:10.3390/agriculture11080732 fatcat:n4kd6fcghzf4xppvsjanexx6be

Detection of Foreign Materials in Wheat Kernels using Regional Texture Descriptors

2019 International journal of recent technology and engineering  
The classification task is performed by the neural classifier in the proposed machine vision system. An accuracy of more than 98.5% is achieved using proposed system.  ...  The present paper reports the development of an efficient machine vision system for automatic detection of foreign materials in wheat kernels using regional texture descriptors.  ...  detecting foreign material represented by barley [10] exclusively.  ... 
doi:10.35940/ijrte.d9499.118419 fatcat:2godgjwmqra35bh2huirr3y6uy

Size distribution of barley kernels

A. Sýkorová, E. Šárka, Z. Bubník, M. Schejbal, P. Dostálek
2009 Czech Journal of Food Sciences  
The measured data were then used to compute the volume and surface area of each of the five kernel models (Models 0–4), the results being subsequently verified by pycnometric measurement.  ...  Barley primarily serves as a major animal feed crop; smaller amounts of barley are used in health foods and in the malting process.  ...  Computer vision is one of such non-destructive methods that involve image analyses and image processing operations (Koc 2007) .  ... 
doi:10.17221/26/2009-cjfs fatcat:da7uhteb2rc47eavvj2plpsgee

Assessment of seed quality using non-destructive measurement techniques: a review

Anisur Rahman, Byoung-Kwan Cho
2016 Seed Science Research  
This review focuses primarily on non-destructive techniques, namely machine vision, spectroscopy, hyperspectral imaging, soft X-ray imaging, thermal imaging and electronic nose techniques, for assessing  ...  Seed quality, including chemical composition, variety identification and classification, insect damage and disease assessment as well as seed viability and germinability of various seeds are discussed.  ...  Financial support This research was partially supported by the Export Strategy Conflicts of interest None.  ... 
doi:10.1017/s0960258516000234 fatcat:x4wgzxwedveuziaam4obekiquq

Identification of Rice Quality Through Pattern Classification Using Computer Vision Image Processing

2020 Computer Engineering and Intelligent Systems  
In this study Computer Vision Image Processing tool applied on three different types of rice. Using this tool we apply pattern classification using nearest neighbor and K-nearest neighbor algorithm.  ...  Using these algorithms we get results of three varieties of rice Bastmati, Jasmine and White rice. In both algorithms white rice result show best from Basmati rice and Jasmine rice.  ...  Using this version of computer vision image processing tool we step by step precede our work which snapshots attached below and explained.  ... 
doi:10.7176/ceis/11-2-01 fatcat:63oyqapxhjchnhn2cgtfzqcw2i

Application of image texture analysis for varietal classification of barley

P. Zapotoczny
2012 International Agrophysics  
Application of image texture analysis for varietal classification of barley This paper presents the results of a study into the use of the texture parameters of barley kernel images in varietal classification  ...  The results were processed statistically by variable reduction and general discriminant analysis. Classification accuracy was more than 99%.  ...  The overall aim of this research was to develop a machine vision system for identifying 5 varieties of Polish spring barley.  ... 
doi:10.2478/v10247-012-0012-z fatcat:7ohykhnkvjen7bw3nylksx622e

Invited Review: Automated seed identification with computer vision: challenges and opportunities

Liang Zhao, S.M. Rafizul Haque, Ruojing Wang
2022 Seed science and technology  
A concept flow chart for using computer vision systems is proposed to advance computer-assisted seed identification.  ...  Applying advanced technologies such as computer vision is highly desirable in seed testing.  ...  Computer vision, a field of computer science, enables computers to emulate human vision by acquiring a high-level understanding of digital images or videos and being able to make inferences and take actions  ... 
doi:10.15258/sst.2022.50.1.s.05 fatcat:saud3jolgnembjkakvqv5bq2ny
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