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Jin C, Zhou X, He M, Li C, Cai Z, Zhou L, Qi H, Zhang C. A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2024. [PMID: 39031773 DOI: 10.1002/jsfa.13668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 07/22/2024]
Abstract
BACKGROUND Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. RESULTS This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. CONCLUSION The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Chen Jin
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Xinyue Zhou
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Mengyu He
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Cheng Li
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Zeyi Cai
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Lei Zhou
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Hengnian Qi
- School of Information Engineering, Huzhou University, Huzhou, China
| | - Chu Zhang
- School of Information Engineering, Huzhou University, Huzhou, China
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Review of technology advances to assess rice quality traits and consumer perception. Food Res Int 2023; 172:113105. [PMID: 37689840 DOI: 10.1016/j.foodres.2023.113105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 06/02/2023] [Accepted: 06/09/2023] [Indexed: 09/11/2023]
Abstract
The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Department of Agrotechnology, Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis, 02600 Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, University of Melbourne, Parkville, VIC 3010, Australia; Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., México 64849, Mexico.
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Gonzalez Viejo C, Torrico DD, Fuentes S. Novel Contactless Sensors for Food, Beverage and Packaging Evaluation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8082. [PMID: 37836912 PMCID: PMC10574833 DOI: 10.3390/s23198082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
The use of traditional methods to evaluate food, beverages, and packaging tends to be time-consuming, labour-intensive, and usually involves high costs due to the need for expensive equipment, regular refill of consumables, skilled personnel and, in the case of sensory evaluation, incentives or payments involved for participants recruitment and/or panelists training and participation [...].
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Affiliation(s)
- Claudia Gonzalez Viejo
- Digital Agriculture Food and Wine, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, VIC 3010, Australia;
| | - Damir D. Torrico
- Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New Zealand;
| | - Sigfredo Fuentes
- Digital Agriculture Food and Wine, School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, Parkville, VIC 3010, Australia;
- Tecnologico de Monterrey, School of Engineering and Science, Ave. Eugenio Garza Sada 2501, Monterrey, NL 64849, Mexico
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Abu-Alruz K, Salameh N. Evaluation of commercial rice grains present in the Amman market. POTRAVINARSTVO 2023. [DOI: 10.5219/1834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Rice is a staple food that contributes to significant energy intake. Jordan relies on importing to provide the market with the required quantities of rice. Different varieties from different sources with various qualities are available in the market. This study aimed to evaluate the quality of rice available in the markets in Amman city-Jordan. Twenty-five brands (three samples from each brand) were collected. Samples were evaluated regarding chemical composition, dimensions before and after cooking, percentage of different defects, pasting profile (pasting temperature, peak viscosity, peak time, trough, and final viscosity), whiteness, transparency, and milling degree. All rice samples tested comply with the Jordanian standard except for chalky kernels (four brands), heat-damaged kernels (one brand), and insect infestation (two brands). All samples that did not fulfil the Jordanian specifications were from the long-grain rice. Medium-grain rice has higher whiteness, transparency, milling degree, moisture, starch, peak viscosity, trough, and final viscosity than long-grain rice. On the other hand, long-grain rice has a higher protein, pasting temperature, and peak time. There were significant differences in pasting and chemical composition parameters within the two groups of grain sizes. The average elongation ratio for all samples was 1.57 ±0.14, with significant differences between different brands. Due to the higher pasting temperature and peak time, long-grain rice requires more energy during cooking than medium-grain rice.
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Chandran AKN, Sandhu J, Irvin L, Paul P, Dhatt BK, Hussain W, Gao T, Staswick P, Yu H, Morota G, Walia H. Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress. FRONTIERS IN PLANT SCIENCE 2022; 13:1026472. [PMID: 36304400 PMCID: PMC9593041 DOI: 10.3389/fpls.2022.1026472] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress.
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Affiliation(s)
| | - Jaspreet Sandhu
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Larissa Irvin
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Puneet Paul
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Balpreet K. Dhatt
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Banos, Philippines
| | - Tian Gao
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Paul Staswick
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Hongfeng Yu
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Gota Morota
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States
| | - Harkamal Walia
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
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Boniecki P, Sujak A, Pilarska AA, Piekarska-Boniecka H, Wawrzyniak A, Raba B. Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains. SENSORS (BASEL, SWITZERLAND) 2022; 22:6578. [PMID: 36081052 PMCID: PMC9459746 DOI: 10.3390/s22176578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/17/2022] [Accepted: 08/27/2022] [Indexed: 06/15/2023]
Abstract
The paper covers the problem of determination of defects and contamination in malting barley grains. The analysis of the problem indicated that although several attempts have been made, there are still no effective methods of identification of the quality of barley grains, such as the use of information technology, including intelligent sensors (currently, quality assessment of grain is performed manually). The aim of the study was the construction of a reduced set of the most important graphic descriptors from machine-collected digital images, important in the process of neural evaluation of the quality of BOJOS variety malting barley. Grains were sorted into three size fractions and seed images were collected. As a large number of graphic descriptors implied difficulties in the development and operation of neural classifiers, a PCA (Principal Component Analysis) statistical method of reducing empirical data contained in the analyzed set was applied. The grain quality expressed by an optimal set of transformed descriptors was modelled using artificial neural networks (ANN). The input layer consisted of eight neurons with a linear Postsynaptic Function (PSP) and a linear activation function. The one hidden layer was composed of sigmoid neurons having a linear PSP function and a logistic activation function. One sigmoid neuron was the output of the network. The results obtained show that neural identification of digital images with application of Principal Component Analysis (PCA) combined with neural classification is an effective tool supporting the process of rapid and reliable quality assessment of BOJOS malting barley grains.
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Affiliation(s)
- Piotr Boniecki
- Department of Biosystems Engineering, Poznań University of Life Sciences, 50 Wojska Polskiego Str., 60-627 Poznań, Poland
| | - Agnieszka Sujak
- Department of Biosystems Engineering, Poznań University of Life Sciences, 50 Wojska Polskiego Str., 60-627 Poznań, Poland
| | - Agnieszka A. Pilarska
- Department of Hydraulic and Sanitary Engineering, Poznań University of Life Sciences, 94A Piątkowska Str., 60-649 Poznań, Poland
| | - Hanna Piekarska-Boniecka
- Department of Entomology and Environmental Protection, Poznań University of Life Sciences, 159 Dąbrowskiego Str., 60-594 Poznań, Poland
| | - Agnieszka Wawrzyniak
- Department of Biosystems Engineering, Poznań University of Life Sciences, 50 Wojska Polskiego Str., 60-627 Poznań, Poland
| | - Barbara Raba
- Department of Biosystems Engineering, Poznań University of Life Sciences, 50 Wojska Polskiego Str., 60-627 Poznań, Poland
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Aznan A, Gonzalez Viejo C, Pang A, Fuentes S. Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies. Foods 2022; 11:foods11091181. [PMID: 35563907 PMCID: PMC9105373 DOI: 10.3390/foods11091181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/18/2022] [Accepted: 04/18/2022] [Indexed: 12/10/2022] Open
Abstract
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.
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Affiliation(s)
- Aimi Aznan
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
- Faculty of Chemical Engineering Technology, University Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Claudia Gonzalez Viejo
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Alexis Pang
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
| | - Sigfredo Fuentes
- Digital Agriculture, Food and Wine Group (DAFW), School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia; (A.A.); (C.G.V.); (A.P.)
- Correspondence: ; Tel.: +61-42-450-4434
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