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Gierz Ł, Al-Sammarraie MAJ, Özbek O, Markowski P. The use of image analysis to study the effect of moisture content on the physical properties of grains. Sci Rep 2024; 14:11673. [PMID: 38778037 PMCID: PMC11111774 DOI: 10.1038/s41598-024-60852-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture content in bean and corn seeds based on measuring their dimensions using image analysis using artificial neural networks (ANN). Experimental tests were carried out at three levels of wet basis moisture content of seeds: 9, 13 and 17%. The analysis of the results showed a direct relationship between the wet basis moisture content and the main dimensions of the seeds. Based on the statistical analysis of the seed material, it was shown that the characteristics examined have a normal or close to normal distribution, and the seed material used in the investigation is representative. Furthermore, the use of artificial neural networks to predict the wet basis moisture content of seeds based on changes in their dimensions has an efficiency of 82%. The results obtained from the method used in this work are very promising for predicting the moisture content.
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Affiliation(s)
- Łukasz Gierz
- Institute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, ul. Piotrowo 3, 60-965, Poznan, Poland.
| | - Mustafa Ahmed Jalal Al-Sammarraie
- Department of Agricultural Machinery and Equipment, College of Agricultural Engineering Sciences, University of Baghdad, Baghdad, Iraq
| | - Osman Özbek
- Department of Agricultural Machineries and Technologies Engineering, Faculty of Agriculture, Selçuk University, 42250, Konya, Turkey
| | - Piotr Markowski
- Department of Heavy-Duty Machines and Research Methodology, University of Warmia and Mazury, ul. Oczapowskiego 11, 10-719, Olsztyn, Poland
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Khan T, Jamil M, Ali A, Rasheed S, Irshad A, Maqsood MF, Zulfiqar U, Chaudhary T, Ali MA, Elshikh MS. Exploring water-absorbing capacity: a digital image analysis of seeds from 120 wheat varieties. Sci Rep 2024; 14:6757. [PMID: 38514746 PMCID: PMC10957954 DOI: 10.1038/s41598-024-57193-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/15/2024] [Indexed: 03/23/2024] Open
Abstract
Wheat is a staple food crop that provides a significant portion of the world's daily caloric intake, serving as a vital source of carbohydrates and dietary fiber for billions of people. Seed shape studies of wheat typically involve the use of digital image analysis software to quantify various seed shape parameters such as length, width, area, aspect ratio, roundness, and symmetry. This study presents a comprehensive investigation into the water-absorbing capacity of seeds from 120 distinct wheat lines, leveraging digital image analysis techniques facilitated by SmartGrain software. Water absorption is a pivotal process in the early stages of seed germination, directly influencing plant growth and crop yield. SmartGrain, a powerful image analysis tool, was employed to extract precise quantitative data from digital images of wheat seeds, enabling the assessment of various seed traits in relation to their water-absorbing capacity. The analysis revealed significant transformations in seed characteristics as they absorbed water, including changes in size, weight, shape, and more. Through statistical analysis and correlation assessments, we identified robust relationships between these seed traits, both before and after water treatment. Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) were employed to categorize genotypes with similar trait patterns, providing insights valuable for crop breeding and genetic research. Multiple linear regression analysis further elucidated the influence of specific seed traits, such as weight, width, and distance, on water-absorbing capacity. Our study contributes to a deeper understanding of seed development, imbibition, and the crucial role of water absorption in wheat. These insights have practical implications in agriculture, offering opportunities to optimize breeding programs for improved water absorption in wheat genotypes. The integration of SmartGrain software with advanced statistical methods enhances the reliability and significance of our findings, paving the way for more efficient and resilient wheat crop production. Significant changes in wheat seed shape parameters were observed after imbibition, with notable increases in area, perimeter, length, width, and weight. The length-to-width ratio (LWR) and circularity displayed opposite trends, with higher values before imbibition and lower values after imbibition.
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Affiliation(s)
- Tooba Khan
- Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Muhammad Jamil
- Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, Pakistan.
| | - Aamir Ali
- Department of Botany, University of Sargodha, Sargodha, Pakistan
| | - Sana Rasheed
- Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Asma Irshad
- Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | | | - Usman Zulfiqar
- Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
| | - Talha Chaudhary
- Faculty of Agricultural and Environmental Sciences, Hungarian University of Agriculture and Life Sciences, 2100, Godollo, Hungary.
| | - M Ajmal Ali
- Department of Botany and Microbiology, College of Science, King Saud University, 11451, Riyadh, Saudi Arabia
| | - Mohamed S Elshikh
- Department of Botany and Microbiology, College of Science, King Saud University, 11451, Riyadh, Saudi Arabia
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Gierz Ł, Przybył K. Texture analysis and artificial neural networks for identification of cereals-case study: wheat, barley and rape seeds. Sci Rep 2022; 12:19316. [PMID: 36369273 PMCID: PMC9652407 DOI: 10.1038/s41598-022-23838-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
Abstract
The scope of the research comprises an analysis and evaluation of samples of rape, barley and wheat seeds. The experiments were carried out using the author's original research object. The air flow velocities to transport seeds, were set at 15, 20 and 25 m s-1. A database consisting of images was created, which allowed to determine 3 classes of kernels on the basis of 6 research variants, including their transportation way via pipe and the speed of sowing. The process of creating neural models was based on multilayer perceptron networks (MLPN) in Statistica (machine learning). It should be added that the use of MLPN also allowed identification of rape seeds, wheat seeds and barley seeds transported via pipe II at 20 m s-1, for which the lowest RMS was 0.05 and the coefficient of classification accuracy was 0.94.
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Affiliation(s)
- Ł Gierz
- Institute of Machine Design, Faculty of Mechanical Engineering, Poznan University of Technology, ul. Piotrowo 3, 60-965, Poznan, Poland.
| | - K Przybył
- Department of Dairy and Process Engineering, Poznan University of Life Sciences, Food Sciences and Nutrition, Wojska Polskiego 31, 60-624, Poznan, Poland
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Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12105071] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area.
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Automated rubber seed ventral surface identification using hue, saturation, value (HSV) image processing and a decision rule approach. J RUBBER RES 2022. [DOI: 10.1007/s42464-022-00155-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Barboza da Silva C, Oliveira NM, de Carvalho MEA, de Medeiros AD, de Lima Nogueira M, Dos Reis AR. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality. Sci Rep 2021; 11:17834. [PMID: 34497292 PMCID: PMC8426380 DOI: 10.1038/s41598-021-97223-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 08/13/2021] [Indexed: 12/03/2022] Open
Abstract
In the agricultural industry, advances in optical imaging technologies based on rapid and non-destructive approaches have contributed to increase food production for the growing population. The present study employed autofluorescence-spectral imaging and machine learning algorithms to develop distinct models for classification of soybean seeds differing in physiological quality after artificial aging. Autofluorescence signals from the 365/400 nm excitation-emission combination (that exhibited a perfect correlation with the total phenols in the embryo) were efficiently able to segregate treatments. Furthermore, it was also possible to demonstrate a strong correlation between autofluorescence-spectral data and several quality indicators, such as early germination and seed tolerance to stressful conditions. The machine learning models developed based on artificial neural network, support vector machine or linear discriminant analysis showed high performance (0.99 accuracy) for classifying seeds with different quality levels. Taken together, our study shows that the physiological potential of soybean seeds is reduced accompanied by changes in the concentration and, probably in the structure of autofluorescent compounds. In addition, altering the autofluorescent properties in seeds impact the photosynthesis apparatus in seedlings. From the practical point of view, autofluorescence-based imaging can be used to check modifications in the optical properties of soybean seed tissues and to consistently discriminate high-and low-vigor seeds.
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Affiliation(s)
- Clíssia Barboza da Silva
- Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba, SP, 13416-000, Brazil.
| | - Nielsen Moreira Oliveira
- Department of Crop Science, College of Agriculture Luiz de Queiroz (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil
| | - Marcia Eugenia Amaral de Carvalho
- Department of Genetics, College of Agriculture Luiz de Queiroz (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil
| | | | - Marina de Lima Nogueira
- Department of Genetics, College of Agriculture Luiz de Queiroz (ESALQ), University of São Paulo (USP), Piracicaba, SP, 13418-900, Brazil
| | - André Rodrigues Dos Reis
- Department of Biosystems Engineering, School of Sciences and Engineering, São Paulo State University (UNESP), Tupã, SP, 17602-496, Brazil
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Przybył K, Koszela K, Adamski F, Samborska K, Walkowiak K, Polarczyk M. Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders. SENSORS (BASEL, SWITZERLAND) 2021; 21:5823. [PMID: 34502718 PMCID: PMC8434077 DOI: 10.3390/s21175823] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 08/16/2021] [Accepted: 08/24/2021] [Indexed: 11/27/2022]
Abstract
In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.
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Affiliation(s)
- Krzysztof Przybył
- Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland; (K.P.); (F.A.)
| | - Krzysztof Koszela
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland
| | - Franciszek Adamski
- Food Sciences and Nutrition, Department of Food Technology of Plant Origin, Poznan University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland; (K.P.); (F.A.)
| | - Katarzyna Samborska
- Institute of Food Sciences, Warsaw University of Life Sciences WULS-SGGW, Nowoursynowska 159c, 02-787 Warsaw, Poland;
| | - Katarzyna Walkowiak
- Food Sciences and Nutrition, Department of Physics and Biophysics, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznan, Poland;
| | - Mariusz Polarczyk
- Main Library and Scientific Information Centre, Poznan University of Life Sciences, Witosa 45, 61-693 Poznan, Poland;
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