scholarly journals Size and mass prediction of almond kernels using machine learning image processing

2019 ◽  
Author(s):  
Sriram K Vidyarthi ◽  
Rakhee Tiwari ◽  
Samrendra K Singh
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Average Length ◽  
Ensemble Model ◽  
Image Processing Algorithm ◽  
Yield And Quality ◽  
Digital Fingerprint ◽  
The Mean ◽  
The Individual ◽  
Vital Parameters

AbstractAfter harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality, particularly head almond yield. In this study, we propose a novel methodology that combines image processing and machine-learning ensemble that accurately measures the size and mass of whole raw almond kernels (classification - Nonpareil) simultaneously. We have developed an image-processing algorithm using recursive method to identify the individual almond kernels from an image and estimate the size of the kernels based on the occupied pixels by a kernel. The number of pixels representing an almond kernel was used as its digital fingerprint to predict its size and mass. Various popular machine learning (ML) models were implemented to build a stacked ensemble model (SEM), predicting the mass of the individual almond kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of image processing and SEM were analyzed using uncertainty quantification. The mean error in estimating the average length of 1000 almond kernel was 3.12%. Similarly, mean errors associated with predicting the 1000 kernel mass were 0.63%. The developed algorithm in almond imaging in this study can be used to facilitate a rapid almond yield and quality appraisals.

scholarly journals Machine learnt image processing to predict weight and size of rice kernels

2019 ◽  
Author(s):  
Samrendra K Singh ◽  
Sriram K Vidyarthi ◽  
Rakhee Tiwari
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Uncertainty Quantification ◽  
Rice Yield ◽  
Image Processing Algorithm ◽  
Rice Quality ◽  
Head Rice ◽  
Kernel Length ◽  
Head Rice Yield ◽  
The Individual

AbstractAccurate measurement of rice kernel sizes after milling is critical to design, develop and optimize rice milling operations. The size and mass of the individual rice kernels are important parameters typically associated with rice quality attributes, particularly head rice yield. In this study, we propose a novel methodology that combines image processing and machine learning (ML) ensemble to accurately measure the size and mass of several rice kernels simultaneously. We have developed an image processing algorithm with the help of recursive method to identify the individual rice kernels from an image and estimate the size of the kernels based on the pixels a kernel occupies. The number of pixels representing a rice kernel has been used as its digital fingerprint in order to predict its size and mass. We have employed a number of popular machine learning models to build a stacked ensemble model (SEM), which can predict the mass of the individual rice kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of our image processing and SEM are quantified using uncertainty quantification. The uncertainty quantification showed 3.6%, 2.5%, and 2.4% for mean errors in estimating the kernel length of small-grain (Calhikari-202), medium-grain (Jupiter), and long-grain (CL153) rice, respectively. Similarly, mean errors associated with predicting the 1000 grain weight are 4.1%, 2.9%, and 4.3% for Calhikari-202, Jupiter, and CL153, respectively. Use of the developed algorithm in rice imaging analyzers could facilitate head rice yield quantifications and promote quicker rice quality appraisals.

scholarly journals Comparative analysis of image processing techniques for obstacle avoidance and path deduction

2021 ◽  
Vol 2070 (1) ◽  
pp. 012121
Author(s):  
R Rajavarshini ◽  
S Shruthi ◽  
P Mahanth ◽  
Boddu Chaitanya Kumar ◽  
A Suyampulingam
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Obstacle Avoidance ◽  
Transportation System ◽  
Image Processing Algorithm ◽  
Image Processing Techniques ◽  
Haar Cascade ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Processing Techniques

Abstract The growing need for automation has a significant impact on our daily lives. Automating the essentials of our society like transportation system has plenty of applications like unmanned ground vehicles in military, wheel chair for disabled, domestic robots, etc., There are driving, braking, obstacle tackling etc., to a transportation system that can be automated. This paper particularly focuses on automating the obstacle avoidance which provides intelligence to the vehicle and ensures a high degree of safety and is performed using image processing algorithms. Edge based detection, image segmentation, and Machine Learning based method are the three image processing techniques used to detect and avoid obstacles. Haar cascade classifier is the machine learning method where Haar cascade analysis is performed for better accurate results with justifying graphs and parametric values obtained. A comparison of the three image processing algorithms is also tabulated considering obstacle size, colour, familiarities and environmental lightings and the best image processing algorithm is inferred.

Publishing in International Journal of Automation Technology

2007 ◽  
Vol 1 (1) ◽  
pp. 4-4
Author(s):  
Yusuf Altintas
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Position Control ◽  
Mechanical Design ◽  
Image Processing Algorithm ◽  
Academic Knowledge ◽  
Software Modules ◽  
Integration Of Disciplines ◽  
The Individual ◽  
Automation Technology

Automation technology is created by integrating mechanical design, dynamics, control, sensors, actuators, electronics and real time software engineering knowledge into a single system. While there are a number of journals which focus on the individual subjects, a sole journal like IJAT which presents the integration of disciplines to create automation products has been missing. Although automation covers rather a large spectrum, we encourage the authors to submit their articles with the details of technology integration. While mathematical details of a position control of a single axis machine may be more suitable to be presented in a pure control journal, the integration of mechanical drives, motors, sensors, control law, trajectory generation and real time software modules constitutes an excellent example for an automation technology. Similarly, while an image processing algorithm would be narrow, integration of image processing, timing, coordination with moving machinery, hardware and software lay out describes an automation technology. The aim of the journal is to bring theory, design and integration together which leads to the creation of a novel automation technology. The journal is expected to be a key resource for automation engineers in industry and academia while disseminating archival academic knowledge to the society.

scholarly journals The assignment success for 22 horse breeds registered in the Czech Republic: The machine learning perspective

2021 ◽  
Vol 66 (No. 1) ◽  
pp. 1-12
Author(s):  
Lenka Štohlová Putnová ◽  
Radek Štohl
Keyword(s):  
Machine Learning ◽  
Czech Republic ◽  
Gene Diversity ◽  
Population Data ◽  
Genetic Distances ◽  
Mean Value ◽  
Data Sets ◽  
The Czech Republic ◽  
The Mean ◽  
The Individual

The paper demonstrates the dependability of assignment testing in the identification of an appropriate breed to monitor comprehensive genetic information from molecular markers to analyse the collection of real population data covering 22 horse breeds registered in the Czech Republic, including native breeds and genetic resources. If 17 microsatellites are used, the mean number of alleles per locus corresponds to 10.4. The count of alleles at the individual loci ranges between five (HTG07) and 17 (ASB17). The loci ASB02, ASB23, HMS03, HTG10, and VHL20 exhibit the highest gene diversity and observed heterozygosity (both above 80%), with the mean value of 0.77 and 0.73, respectively. The moderate total inbreeding coefficient (5.2%) is estimated across all the loci and breeds. The levels of apparent breed differentiation span from zero between the Czech Warmblood and Slovak Warmblood to 0.15 between the Shetland Pony and Standardbred. The phylogenetic breed relationships are revealed via the NeighbourNet dendrogram constructed from Reynolds’ genetic distances, which clearly separate the Coldblood draught, Hot/Warmblood, and Pony horses. Our results reveal that the Bayesian approach (the Rannala and Mountain technique) provides the most intensive prediction power (83.6%) out of the GeneClass tools and that the Bayes Net algorithm exhibits the best efficiency (78.4%) from the WEKA machine learning workbench options, considering the use of the five-fold cross validation technique. The algorithms could be trained on large real reference data sets, and thus there appears another viable perspective for machine learning in horse ancestry testing. In this context, it is also important to stress the fact that innovated computational tools will potentially lead towards structuring a novel web server to allow the identification of horse breeds.

scholarly journals Image Processing in DNA

2019 ◽  
Author(s):  
Chao Pan ◽  
S. M. Hossein Tabatabaei Yazdi ◽  
S Kasra Tabatabaei ◽  
Alvaro G. Hernandez ◽  
Charles Schroeder ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Signal Processing ◽  
Data Storage ◽  
Storage Systems ◽  
Image Inpainting ◽  
Machine Learning Techniques ◽  
Synthetic Dna ◽  
Learning Techniques ◽  
The Individual

ABSTRACTThe main obstacles for the practical deployment of DNA-based data storage platforms are the prohibitively high cost of synthetic DNA and the large number of errors introduced during synthesis. In particular, synthetic DNA products contain both individual oligo (fragment) symbol errors as well as missing DNA oligo errors, with rates that exceed those of modern storage systems by orders of magnitude. These errors can be corrected either through the use of a large number of redundant oligos or through cycles of writing, reading, and rewriting of information that eliminate the errors. Both approaches add to the overall storage cost and are hence undesirable. Here we propose the first method for storing quantized images in DNA that uses signal processing and machine learning techniques to deal with error and cost issues without resorting to the use of redundant oligos or rewriting. Our methods rely on decoupling the RGB channels of images, performing specialized quantization and compression on the individual color channels, and using new discoloration detection and image inpainting techniques. We demonstrate the performance of our approach experimentally on a collection of movie posters stored in DNA.

Pilonidal Sinus: How to Choose Between Excision and Open Granulation Versus Excision and Primary Closure?

Swiss Surgery ◽  
2002 ◽  
Vol 8 (6) ◽  
pp. 255-258 ◽  
Author(s):  
Perruchoud ◽  
Vuilleumier ◽  
Givel
Keyword(s):  
Hospital Stay ◽  
Pilonidal Sinus ◽  
Primary Closure ◽  
Average Length ◽  
Length Of Hospital Stay ◽  
Healing Time ◽  
Incision And Drainage ◽  
The Mean ◽  
Primary Healing

Aims: The purpose of this study was to evaluate excision and open granulation versus excision and primary closure as treatments for pilonidal sinus. Subjects and methods: We evaluated a group of 141 patients operated on for a pilonidal sinus between 1991 and 1995. Ninety patients were treated by excision and open granulation, 34 patients by excision and primary closure and 17 patients by incision and drainage, as a unique treatment of an infected pilonidal sinus. Results: The first group, receiving treatment of excision and open granulation, experienced the following outcomes: average length of hospital stay, four days; average healing time; 72 days; average number of post-operative ambulatory visits, 40; average off-work delay, 38 days; and average follow-up time, 43 months. There were five recurrences (6%) in this group during the follow-up period. For the second group treated by excision and primary closure, the corresponding outcome measurements were as follows: average length of hospital stay, four days; average healing time, 23 days; primary healing failure rate, 9%; average number of post-operative ambulatory visits, 6; average off-work delay, 21 days. The average follow-up time was 34 months, and two recurrences (6%) were observed during the follow-up period. In the third group, seventeen patients benefited from an incision and drainage as unique treatment. The mean follow-up was 37 months. Five recurrences (29%) were noticed, requiring a new operation in all the cases. Discussion and conclusion: This series of 141 patients is too limited to permit final conclusions to be drawn concerning significant advantages of one form of treatment compared to the other. Nevertheless, primary closure offers the advantages of quicker healing time, fewer post-operative visits and shorter time off work. When a primary closure can be carried out, it should be routinely considered for socio-economical and comfort reasons.

A Simple Method for the Evaluation of Internal Doses in Nuclear Medicine

1974 ◽  
Vol 13 (02) ◽  
pp. 193-206
Author(s):  
L. Conte ◽  
L. Mombelli ◽  
A. Vanoli
Keyword(s):  
Nuclear Medicine ◽  
Close Agreement ◽  
Whole Body ◽  
Simple Method ◽  
Rapid Estimation ◽  
Absorbed Doses ◽  
The Mean ◽  
The Individual ◽  
Estimation Of Risk

SummaryWe have put forward a method to be used in the field of nuclear medicine, for calculating internally absorbed doses in patients. The simplicity and flexibility of this method allow one to make a rapid estimation of risk both to the individual and to the population. In order to calculate the absorbed doses we based our procedure on the concept of the mean absorbed fraction, taking into account anatomical and functional variability which is highly important in the calculation of internal doses in children. With this aim in mind we prepared tables which take into consideration anatomical differences and which permit the calculation of the mean absorbed doses in the whole body, in the organs accumulating radioactivity, in the gonads and in the marrow; all this for those radionuclides most widely used in nuclear medicine. By comparing our results with dose obtained from the use of M.I.R.D.'s method it can be seen that when the errors inherent in these types of calculation are taken into account, the results of both methods are in close agreement.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

SERUM THYROTROPHIN AND THE RESPONSE TO THYROTROPHIN RELEASING HORMONE IN SYMPTOMLESS AUTOIMMUNE THYROIDITIS AND IN BORDERLINE AND OVERT HYPOTHYROIDISM

1974 ◽  
Vol 75 (2) ◽  
pp. 274-285 ◽  
Author(s):  
A. Gordin ◽  
P. Saarinen ◽  
R. Pelkonen ◽  
B.-A. Lamberg
Keyword(s):  
Autoimmune Thyroiditis ◽  
Elevated Serum ◽  
Mean Value ◽  
Primary Hypothyroidism ◽  
Overt Hypothyroidism ◽  
Thyrotrophin Releasing Hormone ◽  
Releasing Hormone ◽  
The Mean ◽  
The Individual ◽  
Serum Tsh

ABSTRACT Serum thyrotrophin (TSH) was determined by the double-antibody radioimmunoassay in 58 patients with primary hypothyroidism and was found to be elevated in all but 2 patients, one of whom had overt and one clinically borderline hypothyroidism. Six (29%) out of 21 subjects with symptomless autoimmune thyroiditis (SAT) had an elevated serum TSH level. There was little correlation between the severity of the disease and the serum TSH values in individual cases. However, the mean serum TSH value in overt hypothyroidism (93.4 μU/ml) was significantly higher than the mean value both in clinically borderline hypothyroidism (34.4 μU/ml) and in SAT (8.8 μU/ml). The response to the thyrotrophin-releasing hormone (TRH) was increased in all 39 patients with overt or borderline hypothyroidism and in 9 (43 %) of the 21 subjects with SAT. The individual TRH response in these two groups showed a marked overlap, but the mean response was significantly higher in overt (149.5 μU/ml) or clinically borderline hypothyroidism (99.9 μU/ml) than in SAT (35.3 μU/ml). Thus a normal basal TSH level in connection with a normal response to TRH excludes primary hypothyroidism, but nevertheless not all patients with elevated TSH values or increased responses to TRH are clinically hypothyroid.

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