{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T09:47:32Z","timestamp":1782208052108,"version":"3.54.5"},"reference-count":85,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001769","name":"Charles Sturt University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001769","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Accurate, non-destructive assessment of egg quality is critical for ensuring food safety, maintaining product standards, and operational efficiency in commercial poultry production. This paper introduces\n                    <jats:italic>ELMF4EggQ<\/jats:italic>\n                    , an ensemble learning framework that employs multimodal feature fusion to classify egg grade and freshness using only external attributes \u2013 image, shape, and weight. A novel, publicly available dataset of 186 brown-shelled eggs was constructed, with egg grade and freshness levels determined through laboratory-based expert assessments involving internal quality measurements, such as yolk index and Haugh unit. To the best of our knowledge, this is the first study to apply machine learning methods for internal egg quality assessment using only external, non-invasive features, and the first to release a corresponding labeled dataset. The proposed framework integrates deep features extracted from external egg images with structural characteristics such as egg shape and weight, enabling a comprehensive representation of each egg. Image feature extraction is performed using top-performing pre-trained CNN models (ResNet152, DenseNet169, and ResNet152V2), followed by principal component analysis (PCA)-based dimensionality reduction, synthetic minority oversampling technique (SMOTE) augmentation, and classification using multiple machine learning algorithms. An ensemble voting mechanism combines predictions from the best-performing classifiers to enhance overall accuracy. Experimental results demonstrate that the multimodal approach significantly outperforms the image-only and tabular-only (shape and weight) baselines, with the multimodal ensemble approach achieving an accuracy of 82.24% (SD\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\pm {2.41\\%}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) in grade classification and 70.41% (SD\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\pm {3.20\\%}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) in freshness prediction. The framework demonstrates strong potential for real-time, low-cost deployment in commercial egg processing environments. It highlights the feasibility of using computer vision and lightweight structural inputs for scalable, non-invasive egg quality evaluation. All code and data are publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Kenshin-Keeps\/Egg_Quality_Prediction_ELMF4EggQ\" ext-link-type=\"uri\">https:\/\/github.com\/Kenshin-Keeps\/Egg_Quality_Prediction_ELMF4EggQ<\/jats:ext-link>\n                    , promoting transparency, reproducibility, and further research in this domain.\n                  <\/jats:p>","DOI":"10.1007\/s00521-026-12274-x","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T07:59:15Z","timestamp":1782201555000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ELMF4EggQ: ensemble learning with multimodal feature fusion for non-destructive egg quality assessment"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7119-3212","authenticated-orcid":false,"given":"Md Zahim","family":"Hassan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5307-9993","authenticated-orcid":false,"given":"Md.","family":"Osama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-6535","authenticated-orcid":false,"given":"Muhammad Ashad","family":"Kabir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9293-0069","authenticated-orcid":false,"given":"Md. Saiful","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2771-7576","authenticated-orcid":false,"given":"Zannatul","family":"Naim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"12274_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2021.108418","volume":"131","author":"YL Brasil","year":"2022","unstructured":"Brasil YL, Cruz-Tirado JP, Barbin DF (2022) Fast online estimation of quail eggs freshness using portable nir spectrometer and machine learning. Food Control 131:108418. https:\/\/doi.org\/10.1016\/j.foodcont.2021.108418","journal-title":"Food Control"},{"key":"12274_CR2","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.foodchem.2019.05.182","volume":"296","author":"A Sharaf Eddin","year":"2019","unstructured":"Sharaf Eddin A, Ibrahim SA, Tahergorabi R (2019) Egg quality and safety with an overview of edible coating application for egg preservation. Food Chem 296:29\u201339. https:\/\/doi.org\/10.1016\/j.foodchem.2019.05.182","journal-title":"Food Chem"},{"key":"12274_CR3","unstructured":"World Egg Organization: World Egg Day (2025). https:\/\/www.internationalegg.com\/our-work\/world-egg-day\/. Accessed on 2 Oct 2025\u00a0"},{"key":"12274_CR4","doi-asserted-by":"publisher","first-page":"172","DOI":"10.55544\/jrasb.3.1.28","volume":"3","author":"R Rafed","year":"2024","unstructured":"Rafed R, Abedi MH, Mushfiq S (2024) Nutritional value of eggs in human diet. J Res Appl Sci Biotechnol 3:172\u2013176","journal-title":"J Res Appl Sci Biotechnol"},{"key":"12274_CR5","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1007\/s12161-014-9948-x","volume":"8","author":"M Soltani","year":"2015","unstructured":"Soltani M, Omid M, Alimardani R (2015) Egg quality prediction using dielectric and visual properties based on artificial neural network. Food Anal Methods 8:710\u2013717","journal-title":"Food Anal Methods"},{"issue":"1","key":"12274_CR6","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.jfoodeng.2013.03.019","volume":"118","author":"M Omid","year":"2013","unstructured":"Omid M, Soltani M, Dehrouyeh MH, Mohtasebi SS, Ahmadi H (2013) An expert egg grading system based on machine vision and artificial intelligence techniques. J Food Eng 118(1):70\u201377","journal-title":"J Food Eng"},{"key":"12274_CR7","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1590\/1413-70542017413034816","volume":"41","author":"V Feddern","year":"2017","unstructured":"Feddern V, Pr\u00e1 MCD, Mores R, Nicoloso RDS, Coldebella A, Abreu PGD (2017) Egg quality assessment at different storage conditions, seasons and laying hen strains. Cienc Agrotecnol 41:322\u2013333. https:\/\/doi.org\/10.1590\/1413-70542017413034816","journal-title":"Cienc Agrotecnol"},{"issue":"2","key":"12274_CR8","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.inpa.2014.10.002","volume":"1","author":"S Abdanan Mehdizadeh","year":"2014","unstructured":"Abdanan Mehdizadeh S, Minaei S, Hancock NH, Karimi Torshizi MA (2014) An intelligent system for egg quality classification based on visible-infrared transmittance spectroscopy. Information Processing in Agriculture 1(2):105\u2013114. https:\/\/doi.org\/10.1016\/j.inpa.2014.10.002","journal-title":"Information Processing in Agriculture"},{"issue":"3","key":"12274_CR9","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1007\/s11694-018-9760-1","volume":"12","author":"E Nematinia","year":"2018","unstructured":"Nematinia E, Abdanan Mehdizadeh S (2018) Assessment of egg freshness by prediction of haugh unit and albumen ph using an artificial neural network. Journal of Food Measurement and Characterization 12(3):1449\u20131459","journal-title":"Journal of Food Measurement and Characterization"},{"issue":"12","key":"12274_CR10","doi-asserted-by":"publisher","DOI":"10.3390\/s23125530","volume":"23","author":"J Zhang","year":"2023","unstructured":"Zhang J, Lu W, Jian X, Hu Q, Dai D (2023) Nondestructive detection of egg freshness based on infrared thermal imaging. Sensors Basel 23(12):5530","journal-title":"Sensors Basel"},{"issue":"19","key":"12274_CR11","doi-asserted-by":"publisher","first-page":"5484","DOI":"10.3390\/s20195484","volume":"20","author":"D Dai","year":"2020","unstructured":"Dai D, Jiang T, Lu W, Shen X, Xiu R, Zhang J (2020) Nondestructive detection for egg freshness based on hyperspectral scattering image combined with ensemble learning. Sensors 20(19):5484","journal-title":"Sensors"},{"key":"12274_CR12","doi-asserted-by":"crossref","unstructured":"Ab Nasir AF, Sabarudin SS, Majeed APA, Ghani ASA (2018) Automated egg grading system using computer vision: Investigation on weight measure versus shape parameters. In: IOP Conference Series: Materials Science and Engineering, 342 012003 IOP Publishing","DOI":"10.1088\/1757-899X\/342\/1\/012003"},{"issue":"12","key":"12274_CR13","doi-asserted-by":"publisher","first-page":"2446","DOI":"10.24925\/turjaf.v11i12.2446-2451.6559","volume":"11","author":"SH Abac\u0131","year":"2023","unstructured":"Abac\u0131 SH, Tun\u00e7 T, \u00d6nder H, Erensoy K, Sar\u0131ca M (2023) Examination of the relationships between internal and external egg quality traits: A structural equation model. Turkish Journal of Agriculture-Food Science and Technology 11(12):2446\u20132451","journal-title":"Turkish Journal of Agriculture-Food Science and Technology"},{"key":"12274_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117692","volume":"205","author":"E Sehirli","year":"2022","unstructured":"Sehirli E, Arslan K (2022) An application for the classification of egg quality and haugh unit based on characteristic egg features using machine learning models. Expert Syst Appl 205:117692","journal-title":"Expert Syst Appl"},{"key":"12274_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2020.110036","volume":"283","author":"A Nasiri","year":"2020","unstructured":"Nasiri A, Omid M, Taheri-Garavand A (2020) An automatic sorting system for unwashed eggs using deep learning. J Food Eng 283:110036","journal-title":"J Food Eng"},{"issue":"9","key":"12274_CR16","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.3390\/foods14091507","volume":"14","author":"Z Gao","year":"2025","unstructured":"Gao Z, Zheng J, Xu G (2025) Research progress and technological application prospects of comprehensive evaluation methods for egg freshness. Foods 14(9):1507","journal-title":"Foods"},{"issue":"11","key":"12274_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture14112049","volume":"14","author":"T-G Rho","year":"2024","unstructured":"Rho T-G, Cho B-K (2024) Non-destructive evaluation of physicochemical properties for egg freshness: a review. Agriculture 14(11):2049","journal-title":"Agriculture"},{"key":"12274_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodcont.2020.107426","volume":"118","author":"Y Liu","year":"2020","unstructured":"Liu Y, Ren X, Yu H, Cheng Y, Guo Y, Yao W, Xie Y (2020) Non-destructive and online egg freshness assessment from the egg shell based on raman spectroscopy. Food Control 118:107426","journal-title":"Food Control"},{"issue":"22","key":"12274_CR19","doi-asserted-by":"publisher","DOI":"10.3390\/foods13223563","volume":"13","author":"EM Atwa","year":"2024","unstructured":"Atwa EM, Xu S, Rashwan AK, Abdelshafy AM, ElMasry G, Al-Rejaie S, Xu H, Lin H, Pan J (2024) Advances in emerging non-destructive technologies for detecting raw egg freshness: a comprehensive review. Foods 13(22):3563","journal-title":"Foods"},{"key":"12274_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/foods14132223","volume":"14","author":"Q Wang","year":"2025","unstructured":"Wang Q, Yang Z, Liu C, Sun R, Yue S (2025) Research progress on non-destructive testing technology and equipment for poultry eggshell quality. Foods 14:2223. https:\/\/doi.org\/10.3390\/foods14132223","journal-title":"Foods"},{"key":"12274_CR21","doi-asserted-by":"publisher","DOI":"10.3389\/fvets.2023.1133752","volume":"10","author":"H-L Ren","year":"2023","unstructured":"Ren H-L, Zhao X-Y, Di K-Q, Li L-H, Hao E-Y, Chen H, Zhou R-Y, Nie C-S, Wang D-H (2023) Eggshell translucency in late-phase laying hens and its effect on egg quality and physiological indicators. Front Vet Sci 10:2023. https:\/\/doi.org\/10.3389\/fvets.2023.1133752","journal-title":"Front Vet Sci"},{"issue":"206","key":"12274_CR22","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2023.0293","volume":"20","author":"MRG Attard","year":"2023","unstructured":"Attard MRG, Bowen J, Portugal SJ (2023) Surface texture heterogeneity in maculated bird eggshells. J R Soc Interface 20(206):20230293. https:\/\/doi.org\/10.1098\/rsif.2023.0293","journal-title":"J R Soc Interface"},{"issue":"1","key":"12274_CR23","doi-asserted-by":"publisher","first-page":"184","DOI":"10.3390\/foods12010184","volume":"12","author":"L Yuan","year":"2023","unstructured":"Yuan L, Fu X, Yang X, Chen X, Huang G, Chen X, Shi W, Li L (2023) Non-destructive measurement of egg\u2019s haugh unit by vis-nir with ipls-lasso selection. Foods 12(1):184. https:\/\/doi.org\/10.3390\/foods12010184","journal-title":"Foods"},{"key":"12274_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/app11062815","volume":"11","author":"W-T Hsiao","year":"2021","unstructured":"Hsiao W-T, Lin H-H, Lai L-H (2021) Application of visual radiographic analysis of quality grade of table eggs. Appl Sci 11:2815. https:\/\/doi.org\/10.3390\/app11062815","journal-title":"Appl Sci"},{"key":"12274_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2022.111355","volume":"342","author":"S Olakanmi","year":"2023","unstructured":"Olakanmi S, Karunakaran C, Jayas D (2023) Applications of x-ray micro-computed tomography and small-angle x-ray scattering techniques in food systems: A concise review. J Food Eng 342:111355. https:\/\/doi.org\/10.1016\/j.jfoodeng.2022.111355","journal-title":"J Food Eng"},{"issue":"11","key":"12274_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/foods12112179","volume":"12","author":"Y Huang","year":"2023","unstructured":"Huang Y, Luo Y, Cao Y, Lin X, Wei H, Wu M, Yang X, Zhao Z (2023) Damage detection of unwashed eggs through video and deep learning. Foods 12(11):2179","journal-title":"Foods"},{"key":"12274_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106152","volume":"185","author":"M Turkoglu","year":"2021","unstructured":"Turkoglu M (2021) Defective egg detection based on deep features and bidirectional long-short-term-memory. Comput Electron Agric 185:106152","journal-title":"Comput Electron Agric"},{"key":"12274_CR28","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.measurement.2018.09.059","volume":"135","author":"R Mota-Grajales","year":"2019","unstructured":"Mota-Grajales R, Torres-Pe\u00f1a J, Camas-Anzueto J, P\u00e9rez-Patricio M, Couti\u00f1o RG, L\u00f3pez-Estrada F, Escobar-G\u00f3mez E, Guerra-Crespo H (2019) Defect detection in eggshell using a vision system to ensure the incubation in poultry production. Measurement 135:39\u201346","journal-title":"Measurement"},{"key":"12274_CR29","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.compag.2019.01.005","volume":"158","author":"B Guanjun","year":"2019","unstructured":"Guanjun B, Mimi J, Yi X, Shibo C, Qinghua Y (2019) Cracked egg recognition based on machine vision. Comput Electron Agric 158:159\u2013166","journal-title":"Comput Electron Agric"},{"key":"12274_CR30","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.jfoodeng.2017.04.015","volume":"209","author":"J Priyadumkol","year":"2017","unstructured":"Priyadumkol J, Kittichaikarn C, Thainimit S (2017) Crack detection on unwashed eggs using image processing. J Food Eng 209:76\u201382","journal-title":"J Food Eng"},{"key":"12274_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2020.110041","volume":"283","author":"C Okinda","year":"2020","unstructured":"Okinda C, Sun Y, Nyalala I, Korohou T, Opiyo S, Wang J, Shen M (2020) Egg volume estimation based on image processing and computer vision. J Food Eng 283:110041","journal-title":"J Food Eng"},{"key":"12274_CR32","doi-asserted-by":"crossref","unstructured":"Javadikia P, Dehrouyeh MH, Naderloo L, Rabbani H, Lorestani AN (2011) Measuring the weight of egg with image processing and anfis model. In: Swarm, Evolutionary, and Memetic Computing: Second International Conference, SEMCCO 2011, Visakhapatnam, Andhra Pradesh, India, December 19-21, 2011, Proceedings, Part I 2, pp. 407\u2013416 Springer","DOI":"10.1007\/978-3-642-27172-4_50"},{"key":"12274_CR33","doi-asserted-by":"crossref","unstructured":"Thipakorn J, Waranusast R, Riyamongkol P (2017) Egg weight prediction and egg size classification using image processing and machine learning. 14th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, pp 477\u2013480","DOI":"10.1109\/ECTICon.2017.8096278"},{"issue":"3","key":"12274_CR34","first-page":"82","volume":"2","author":"A Aragua","year":"2018","unstructured":"Aragua A, Mabayo V\u0130 (2018) A cost-effective approach for chicken egg weight estimation through computer vision. Int J Agric Environ Food Sci 2(3):82\u201387","journal-title":"Int J Agric Environ Food Sci"},{"key":"12274_CR35","doi-asserted-by":"crossref","unstructured":"Siswantoro J, Hilman M, Widiasri M (2017) Computer vision system for egg volume prediction using backpropagation neural network. In: IOP Conference Series: Materials Science and Engineering, vol. 273, p. 012002 IOP Publishing","DOI":"10.1088\/1757-899X\/245\/1\/012002"},{"key":"12274_CR36","doi-asserted-by":"crossref","unstructured":"Widiasri M, Santoso LP, Siswantoro J (2019) Computer vision system in measurement of the volume and mass of egg using the disc method. In: IOP Conference Series: Materials Science and Engineering, vol. 703, p. 012050 IOP Publishing","DOI":"10.1088\/1757-899X\/703\/1\/012050"},{"key":"12274_CR37","doi-asserted-by":"crossref","unstructured":"Jiang M-l, Wu P-l, Li F (2021) Detecting dark spot eggs based on cnn googlenet model. In: Simulation Tools and Techniques: 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part II 12, pp. 116\u2013126 Springer","DOI":"10.1007\/978-3-030-72795-6_10"},{"issue":"12","key":"12274_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0190054","volume":"12","author":"L Ma","year":"2017","unstructured":"Ma L, Sun K, Tu K, Pan L, Zhang W (2017) Identification of double-yolked duck egg using computer vision. PLoS One 12(12):0190054","journal-title":"PLoS One"},{"key":"12274_CR39","doi-asserted-by":"crossref","unstructured":"Qin H, Wang W, Chu X, Jiang H, Zhao X, Jia B, Yang Y, Kimuli D, Dong A, Wang B, et al.: (2018) Research on the nondestructive detection of egg freshness based on image processing. In: 2018 ASABE Annual International Meeting, p. 1 American Society of Agricultural and Biological Engineers","DOI":"10.13031\/aim.201800829"},{"issue":"19","key":"12274_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/foods11193082","volume":"11","author":"TH Kim","year":"2022","unstructured":"Kim TH, Kim JH, Kim JY, Oh SE (2022) Egg freshness prediction model using real-time cold chain storage condition based on transfer learning. Foods 11(19):3082","journal-title":"Foods"},{"key":"12274_CR41","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1007\/s12161-014-9944-1","volume":"8","author":"L Sun","year":"2015","unstructured":"Sun L, Yuan L-M, Cai J-R, Lin H, Zhao J-W (2015) Egg freshness on-line estimation using machine vision and dynamic weighing. Food Anal Methods 8:922\u2013928","journal-title":"Food Anal Methods"},{"key":"12274_CR42","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-96140-x","volume":"11","author":"S Harnsoongnoen","year":"2021","unstructured":"Harnsoongnoen S, Jaroensuk N (2021) The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor. Sci Rep 11:16640","journal-title":"Sci Rep"},{"key":"12274_CR43","unstructured":"Jones D Haugh unit: gold standard of egg quality. Natl Egg Qual Sch Proc 7:47\u201351"},{"issue":"14","key":"12274_CR44","doi-asserted-by":"publisher","first-page":"2354","DOI":"10.3390\/ani13142354","volume":"13","author":"X Yang","year":"2023","unstructured":"Yang X, Bist RB, Subedi S, Chai L (2023) A computer vision-based automatic system for egg grading and defect detection. Animals 13(14):2354","journal-title":"Animals"},{"issue":"2","key":"12274_CR45","doi-asserted-by":"publisher","first-page":"41","DOI":"10.5923\/j.ijaf.20201002.01","volume":"10","author":"S Sapkota","year":"2020","unstructured":"Sapkota S, Kolakshyapati MR, Devkota NR, Gorkhali NA, Bhattarai N (2020) Evaluation of external and internal egg quality traits of indigenous sakini chicken in different generations of selection. International Journal of Agriculture and Forestry 10(2):41\u201348. https:\/\/doi.org\/10.5923\/j.ijaf.20201002.01","journal-title":"International Journal of Agriculture and Forestry"},{"key":"12274_CR46","unstructured":"Reddy P, Reddy V, Reddy C, Rao P (1981) Egg weight, shape index and hatchability in khaki campbell duck eggs"},{"issue":"3","key":"12274_CR47","doi-asserted-by":"publisher","first-page":"739","DOI":"10.3382\/ps.2011-01639","volume":"91","author":"Q Huang","year":"2012","unstructured":"Huang Q, Qiu N, Ma M, Jin Y, Yang H, Geng F, Sun S (2012) Estimation of egg freshness using s-ovalbumin as an indicator. Poult Sci 91(3):739\u2013743. https:\/\/doi.org\/10.3382\/ps.2011-01639","journal-title":"Poult Sci"},{"key":"12274_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/9562886","volume":"2022","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Fu D, Ma M (2022) Egg freshness indexes correlations with ovomucin concentration during storage. J Food Qual 2022:1\u20138. https:\/\/doi.org\/10.1155\/2022\/9562886","journal-title":"J Food Qual"},{"key":"12274_CR49","doi-asserted-by":"crossref","unstructured":"Mutlag WK, Ali SK, Aydam ZM, Taher BH (2020) Feature extraction methods: a review. In: Journal of Physics: Conference Series, vol. 1591, p. 012028 IOP Publishing","DOI":"10.1088\/1742-6596\/1591\/1\/012028"},{"key":"12274_CR50","doi-asserted-by":"crossref","unstructured":"Dulal R, Zheng L, Kabir MA (2025) Mhaff: Multi-head attention feature fusion of cnn and transformer for cattle identification.arXiv:2501.05209 arXiv preprint","DOI":"10.1109\/TAFE.2025.3574708"},{"key":"12274_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2023.101020","volume":"18","author":"RG Poola","year":"2023","unstructured":"Poola RG, Pl L et al (2023) COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm. Results Eng 18:101020","journal-title":"Results Eng"},{"key":"12274_CR52","doi-asserted-by":"crossref","unstructured":"Chowdhury A, Jiang M, Chaudhuri S, Jermaine C (2021) Few-shot image classification: Just use a library of pre-trained feature extractors and a simple classifier. Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 9445\u20139454","DOI":"10.1109\/ICCV48922.2021.00931"},{"key":"12274_CR53","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, vol 31","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"12274_CR54","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"12274_CR55","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"12274_CR56","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4510\u20134520 (Mobilenetv 2: Inverted residuals and linear bottlenecks)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"12274_CR57","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"12274_CR58","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"12274_CR59","unstructured":"Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning. pp 6105\u20136114 (PMLR)"},{"key":"12274_CR60","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 11976\u201311986","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"12274_CR61","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research 16:321\u2013357","journal-title":"Journal of artificial intelligence research"},{"issue":"3","key":"12274_CR62","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/0098-3004(93)90090-R","volume":"19","author":"A Ma\u0107kiewicz","year":"1993","unstructured":"Ma\u0107kiewicz A, Ratajczak W (1993) Principal components analysis (pca). Computers & Geosciences 19(3):303\u2013342","journal-title":"Computers & Geosciences"},{"key":"12274_CR63","doi-asserted-by":"crossref","unstructured":"Heckler L, K\u00f6nig R, Bergmann P (2023) Exploring the importance of pretrained feature extractors for unsupervised anomaly detection and localization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 2917\u20132926","DOI":"10.1109\/CVPRW59228.2023.00293"},{"key":"12274_CR64","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.neucom.2020.10.068","volume":"423","author":"J Gu\u00e9rin","year":"2021","unstructured":"Gu\u00e9rin J, Thiery S, Nyiri E, Gibaru O, Boots B (2021) Combining pretrained cnn feature extractors to enhance clustering of complex natural images. Neurocomputing 423:551\u2013571","journal-title":"Neurocomputing"},{"key":"12274_CR65","doi-asserted-by":"crossref","unstructured":"Tolentino LKS, Enrico EJG, Listanco RLM, Ramirez MAM, Renon TLU, Samson MRB (2018) Development of fertile egg detection and incubation system using image processing and automatic candling. In: TENCON 2018-2018 IEEE Region 10 Conference, pp. 0701\u20130706 IEEE","DOI":"10.1109\/TENCON.2018.8650320"},{"issue":"6","key":"12274_CR66","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1080\/00071668.2018.1523535","volume":"59","author":"A Vasileva","year":"2018","unstructured":"Vasileva A, Gorbunova E, Vasilev A, Peretyagin V, Chertov A, Korotaev V (2018) Assessing exterior egg quality indicators using machine vision. Br Poult Sci 59(6):636\u2013645","journal-title":"Br Poult Sci"},{"issue":"6","key":"12274_CR67","doi-asserted-by":"publisher","first-page":"925","DOI":"10.3390\/foods13060925","volume":"13","author":"W Tang","year":"2024","unstructured":"Tang W, Zhang H, Chen H, Fan W, Wang Q (2024) A non-destructive detection and grading method of the internal quality of preserved eggs based on an improved convnext. Foods 13(6):925","journal-title":"Foods"},{"key":"12274_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.atech.2022.100161","volume":"4","author":"FP Oliveira-Boreli","year":"2023","unstructured":"Oliveira-Boreli FP, Pereira DF, Gon\u00e7alves JA, Silva VZ, Alencar N\u00e4\u00e4s I (2023) Non-destructive assessment of hens\u2019 eggs quality using image analysis and machine learning. Smart Agricultural Technology 4:100161","journal-title":"Smart Agricultural Technology"},{"issue":"7","key":"12274_CR69","doi-asserted-by":"publisher","first-page":"1824","DOI":"10.3390\/agronomy13071824","volume":"13","author":"G Yang","year":"2023","unstructured":"Yang G, Wang J, Nie Z, Yang H, Yu S (2023) A lightweight yolov8 tomato detection algorithm combining feature enhancement and attention. Agronomy 13(7):1824","journal-title":"Agronomy"},{"issue":"11","key":"12274_CR70","doi-asserted-by":"publisher","first-page":"1987","DOI":"10.3390\/foods14111987","volume":"14","author":"Y Shu","year":"2025","unstructured":"Shu Y, Zhang J, Wang Y, Wei Y (2025) Fruit freshness classification and detection based on the resnet-101 network and non-local attention mechanism. Foods 14(11):1987","journal-title":"Foods"},{"key":"12274_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2025.118374","author":"C Shen","year":"2025","unstructured":"Shen C, Jin Q, Zhou G, Wang R, Wang Z, Liu D, Cai K, Xu B (2025) Advanced deep learning algorithms in food quality and authenticity. TrAC Trends Anal Chem. https:\/\/doi.org\/10.1016\/j.trac.2025.118374","journal-title":"TrAC Trends Anal Chem"},{"key":"12274_CR72","doi-asserted-by":"crossref","unstructured":"Shehzad K, Ali U, Munir A (2025) Computer vision for food quality assessment: Advances and challenges. Available at SSRN 5196776","DOI":"10.2139\/ssrn.5196776"},{"key":"12274_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2021.107252","volume":"93","author":"M Rahman","year":"2021","unstructured":"Rahman M, Cao Y, Sun X, Li B, Hao Y (2021) Deep pre-trained networks as a feature extractor with xgboost to detect tuberculosis from chest x-ray. Computers & Electrical Engineering 93:107252","journal-title":"Computers & Electrical Engineering"},{"issue":"5","key":"12274_CR74","doi-asserted-by":"publisher","first-page":"198","DOI":"10.3390\/computers14050198","volume":"14","author":"X Ouyang","year":"2025","unstructured":"Ouyang X, Zhuang J, Gu J, Ye S (2025) Few-shot data augmentation by morphology-constrained latent diffusion for enhanced nematode recognition. Computers 14(5):198","journal-title":"Computers"},{"issue":"3","key":"12274_CR75","doi-asserted-by":"publisher","first-page":"339","DOI":"10.3390\/plants14030339","volume":"14","author":"H Zhou","year":"2025","unstructured":"Zhou H, Li W, Li P, Xu Y, Zhang L, Zhou X, Zhao Z, Li E, Lv C (2025) A novel few-shot learning framework based on diffusion models for high-accuracy sunflower disease detection and classification. Plants 14(3):339","journal-title":"Plants"},{"key":"12274_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2025.110162","volume":"233","author":"S Zeng","year":"2025","unstructured":"Zeng S, Xia Y, Gu S, Liu F, Zhou J (2025) Few-shot classification for soil images: Prototype correction and feature distance enhancement. Comput Electron Agric 233:110162","journal-title":"Comput Electron Agric"},{"key":"12274_CR77","doi-asserted-by":"crossref","unstructured":"Holliday A, Dudek G (2020) Pre-trained cnns as visual feature extractors: A broad evaluation. CRV, pp 78\u201384","DOI":"10.1109\/CRV50864.2020.00019"},{"key":"12274_CR78","doi-asserted-by":"crossref","unstructured":"Xu G, Wang X, Wu X, Leng X, Xu Y (2024) Development of skip connection in deep neural networks for computer vision and medical image analysis: A survey. arXiv preprint arXiv:2405.01725","DOI":"10.1016\/j.engappai.2024.109890"},{"issue":"18","key":"12274_CR79","doi-asserted-by":"publisher","DOI":"10.3390\/app12188972","volume":"12","author":"M Shafiq","year":"2022","unstructured":"Shafiq M, Gu Z (2022) Deep residual learning for image recognition: a survey. Applied Sciences 12(18):8972","journal-title":"Applied Sciences"},{"key":"12274_CR80","doi-asserted-by":"crossref","unstructured":"Hager P, Menten MJ, Rueckert D (2023) Best of both worlds: Multimodal contrastive learning with tabular and imaging data. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 23924\u201323935","DOI":"10.1109\/CVPR52729.2023.02291"},{"key":"12274_CR81","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.foodqual.2017.08.007","volume":"63","author":"J Aschemann-Witzel","year":"2018","unstructured":"Aschemann-Witzel J (2018) Consumer perception and preference for suboptimal food under the emerging practice of expiration date based pricing in supermarkets. Food Qual Prefer 63:119\u2013128","journal-title":"Food Qual Prefer"},{"issue":"1","key":"12274_CR82","doi-asserted-by":"publisher","DOI":"10.1186\/s13690-023-01175-3","volume":"81","author":"S Vandevijvere","year":"2023","unstructured":"Vandevijvere S, Van Dam I, Ina\u00e7 Y, Smets V (2023) Unhealthy food availability, prominence and promotion in a representative sample of supermarkets in Flanders (Belgium): a detailed assessment. Arch Public Health 81(1):154","journal-title":"Arch Public Health"},{"key":"12274_CR83","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: Transformers for image recognition at scale.arXiv:2010.11929 arXiv preprint"},{"key":"12274_CR84","unstructured":"Parnami A, Lee M (2022) Learning from few examples: A summary of approaches to few-shot learning"},{"key":"12274_CR85","doi-asserted-by":"crossref","unstructured":"Xian Y, Schiele B, Akata Z (2017) Zero-shot learning-the good, the bad and the ugly. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4582\u20134591","DOI":"10.1109\/CVPR.2017.328"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12274-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-12274-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-12274-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T09:04:14Z","timestamp":1782205454000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-12274-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":85,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["12274"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-12274-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]},"assertion":[{"value":"3 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare that they have no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"527"}}