{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T13:09:15Z","timestamp":1775999355797,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T00:00:00Z","timestamp":1697587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Industrial Doctorate grants 2021","award":["PID2022-1374370B-100"],"award-info":[{"award-number":["PID2022-1374370B-100"]}]},{"name":"Ministry of Science and Innovation","award":["PID2022-1374370B-100"],"award-info":[{"award-number":["PID2022-1374370B-100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs).<\/jats:p>","DOI":"10.3390\/s23208562","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T10:36:56Z","timestamp":1697625416000},"page":"8562","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Data Augmentation Techniques for Machine Learning Applied to Optical Spectroscopy Datasets in Agrifood Applications: A Comprehensive Review"],"prefix":"10.3390","volume":"23","author":[{"given":"Ander","family":"Gracia Mois\u00e9s","sequence":"first","affiliation":[{"name":"Department of Electrical, Electronic and Communications Engineering, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"},{"name":"Pyroistech S.L., C\/Tajonar 22, 31006 Pamplona, NA, Spain"}]},{"given":"Ignacio","family":"Vitoria Pascual","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Communications Engineering, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"},{"name":"Pyroistech S.L., C\/Tajonar 22, 31006 Pamplona, NA, Spain"},{"name":"Institute of Smart Cities, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0606-406X","authenticated-orcid":false,"given":"Jos\u00e9 Javier","family":"Imas Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Communications Engineering, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"},{"name":"Institute of Smart Cities, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6601-5449","authenticated-orcid":false,"given":"Carlos","family":"Ruiz Zamarre\u00f1o","sequence":"additional","affiliation":[{"name":"Department of Electrical, Electronic and Communications Engineering, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"},{"name":"Pyroistech S.L., C\/Tajonar 22, 31006 Pamplona, NA, Spain"},{"name":"Institute of Smart Cities, Public University of Navarra, Campus Arrosad\u00eda, 31006 Pamplona, NA, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,18]]},"reference":[{"key":"ref_1","unstructured":"Pavia, D.L., Lampman, G.M., Kriz, G.S., and Vyvyan, J.A. (2022, December 20). Introduction to Spectroscopy. Google Libros. Available online: https:\/\/books.google.es\/books?hl=es&lr=&id=N-zKAgAAQBAJ&oi=fnd&pg=PP1&dq=spectroscopy+&ots=XfmebVhP2L&sig=ressCoxB7WEneEerzZzaUmQfThs#v=onepage&q=spectroscopy&f=false."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8200","DOI":"10.1039\/C4CS00062E","article-title":"Near-Infrared Spectroscopy and Hyperspectral Imaging: Non-Destructive Analysis of Biological Materials","volume":"43","author":"Manley","year":"2014","journal-title":"Chem. Soc. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1016\/j.foodchem.2007.10.014","article-title":"Raman Spectroscopy a Promising Technique for Quality Assessment of Meat and Fish: A Review","volume":"107","author":"Herrero","year":"2008","journal-title":"Food Chem."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7434","DOI":"10.3390\/s100807434","article-title":"Laser Induced Breakdown Spectroscopy for Elemental Analysis in Environmental, Cultural Heritage and Space Applications: A Review of Methods and Results","volume":"10","author":"Gaudiuso","year":"2010","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11889","DOI":"10.3390\/s150511889","article-title":"Fruit Quality Evaluation Using Spectroscopy Technology: A Review","volume":"15","author":"Wang","year":"2015","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"371","DOI":"10.2138\/rmg.2014.78.9","article-title":"Optical Spectroscopy","volume":"78","author":"Rossman","year":"2014","journal-title":"Rev. Mineral. Geochem."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1007\/s00216-020-02407-z","article-title":"A Critical Review of Recent Trends, and a Future Perspective of Optical Spectroscopy as PAT in Biopharmaceutical Downstream Processing","volume":"412","author":"Rolinger","year":"2020","journal-title":"Anal. Bioanal. Chem."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1063\/1.1305516","article-title":"Review of Temperature Measurement","volume":"71","author":"Childs","year":"2000","journal-title":"Rev. Sci. Instrum."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1002\/anie.199304573","article-title":"Optical Spectroscopy of Single Impurity Molecules in Solids","volume":"32","author":"Moerner","year":"1993","journal-title":"Angew. Chem. Int. Ed. Engl."},{"key":"ref_10","unstructured":"Osborne, B.G. (2006). Near-Infrared Spectroscopy in Food Analysis, John Wiley & Sons, Inc."},{"key":"ref_11","first-page":"285","article-title":"Review of Progress in Application Visible\/near-Infrared Spectroscopy in Liquid Food Detection","volume":"28","author":"Lin","year":"2008","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_12","first-page":"1544","article-title":"Recent Progress in NIR Spectroscopy Technology and Its Application to the Field of Forestry","volume":"28","author":"Gong","year":"2008","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_13","first-page":"122","article-title":"Progress in Application of near Infrared Spectroscopy to Nondestructive On-Line Detection of Products\/Food Quality","volume":"29","author":"Sun","year":"2009","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1255\/jnirs.92","article-title":"Visible and near Infrared Reflectance Spectroscopy for the Determination of Moisture, Fat and Protein in Chicken Breast and Thigh Muscle","volume":"4","author":"Cozzolino","year":"1996","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4526","DOI":"10.1080\/10408398.2021.1876624","article-title":"Application of New Emerging Techniques in Combination with Classical Methods for the Determination of the Quality and Authenticity of Olive Oil: A Review","volume":"62","author":"Zaroual","year":"2022","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1080\/10408390802606790","article-title":"The Use of Near-Infrared Spectrometry in the Olive Oil Industry","volume":"50","author":"Armenta","year":"2010","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_17","first-page":"1","article-title":"Formaldehyde","volume":"A11","author":"Franz","year":"2016","journal-title":"Ullmann\u2019s Encycl. Ind. Chem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.saa.2009.10.001","article-title":"Identification of Transgenic Foods Using NIR Spectroscopy: A Review","volume":"75","author":"Alishahi","year":"2010","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chauhan, N.K., and Singh, K. (2018, January 28\u201329). A Review on Conventional Machine Learning vs Deep Learning. Proceedings of the 2018 International Conference on Computing, Power and Communication Technologies, GUCON 2018, Greater Noida, India.","DOI":"10.1109\/GUCON.2018.8675097"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shinde, P.P., and Shah, S. (2018, January 16\u201318). A Review of Machine Learning and Deep Learning Applications. Proceedings of the 2018 4th International Conference on Computing, Communication Control and Automation, ICCUBEA 2018, Pune, India.","DOI":"10.1109\/ICCUBEA.2018.8697857"},{"key":"ref_21","first-page":"733","article-title":"Applications of Machine Learning in Spectroscopy","volume":"56","author":"Greenop","year":"2020","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine Learning in Agriculture: A Review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"767","DOI":"10.3390\/smartcities3030039","article-title":"Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review","volume":"3","author":"Su","year":"2020","journal-title":"Smart Cities"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.jasms.2010.01.010","article-title":"IRPD Spectroscopy and Ensemble Measurements: Effects of Different Data Acquisition and Analysis Methods","volume":"21","author":"Prell","year":"2010","journal-title":"J. Am. Soc. Mass. Spectrom."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1002\/jbio.201400051","article-title":"Optical Hyperspectral Imaging in Microscopy and Spectroscopy\u2014A Review of Data Acquisition","volume":"8","author":"Gao","year":"2015","journal-title":"J. Biophotonics"},{"key":"ref_26","unstructured":"Ur-Rahman, A., Choudhary, M.I., and Wahab, A.-T. (2022, December 20). Solving Problems with NMR Spectroscopy. Google Libros. Available online: https:\/\/books.google.es\/books?hl=es&lr=&id=2PujBwAAQBAJ&oi=fnd&pg=PP1&dq=problems+spectroscopy&ots=TETTQ5BDlo&sig=exLWLZSyJKQMl6bJcrmhoiP2M8M#v=onepage&q=problems%20spectroscopy&f=false."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_28","unstructured":"Chawla, N.V. (2009). Data Mining and Knowledge Discovery Handbook, Springer."},{"key":"ref_29","first-page":"305","article-title":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning","volume":"19","author":"Goodfellow","year":"2017","journal-title":"Genet. Program. Evolvable Mach."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from Imbalanced Data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A Survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","article-title":"A Review: Data Pre-Processing and Data Augmentation Techniques","volume":"3","author":"Maharana","year":"2022","journal-title":"Glob. Transit. Proc."},{"key":"ref_33","first-page":"95","article-title":"Further Advantages of Data Augmentation on Convolutional Neural Networks","volume":"Volume 11139","year":"2018","journal-title":"Artificial Neural Networks and Machine Learning\u2014ICANN 2018, Proceedings of the 27th International Conference on Artificial Neural Networks, Rhodes, Greece, 4\u20137 October 2018"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wong, S.C., Gatt, A., Stamatescu, V., and McDonnell, M.D. (December, January 30). Understanding Data Augmentation for Classification: When to Warp?. Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, Gold Coast, QLD, Australia.","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s10845-018-1456-1","article-title":"Intelligent Rotating Machinery Fault Diagnosis Based on Deep Learning Using Data Augmentation","volume":"31","author":"Li","year":"2020","journal-title":"J. Intell. Manuf."},{"key":"ref_36","unstructured":"(2023, July 28). Learning Internal Representations by Error Propagation. Available online: https:\/\/apps.dtic.mil\/sti\/citations\/ADA164453."},{"key":"ref_37","first-page":"1","article-title":"Backpropagation Algorithm: An Artificial Neural Network Approach for Pattern Recognition","volume":"3","author":"Kishore","year":"2012","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_38","unstructured":"Ruder, S. (2016). An Overview of Gradient Descent Optimization Algorithms. arXiv."},{"key":"ref_39","first-page":"2715","article-title":"Comparison of Optimization Techniques Based on Gradient Descent Algorithm: A Review","volume":"18","author":"Haji","year":"2021","journal-title":"PalArch\u2019s J. Archaeol. Egypt\/Egyptol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1080\/09599916.2020.1858937","article-title":"Metrics for Evaluating the Performance of Machine Learning Based Automated Valuation Models","volume":"38","author":"Steurer","year":"2021","journal-title":"J. Prop. Res."},{"key":"ref_41","unstructured":"Vickery, R. (2023, July 28). 8 Metrics to Measure Classification Performance. Towards Data Science. Available online: https:\/\/towardsdatascience.com\/8-metrics-to-measure-classification-performance-984d9d7fd7aa."},{"key":"ref_42","unstructured":"Flach, P.A., and Kull, M. (2015, January 7\u201312). Precision-Recall-Gain Curves: PR Analysis Done Right. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0169-7439(98)00071-9","article-title":"Data Augmentation: An Alternative Approach to the Analysis of Spectroscopic Data","volume":"44","author":"Conlin","year":"1998","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.chemolab.2012.07.010","article-title":"A Review of Variable Selection Methods in Partial Least Squares Regression","volume":"118","author":"Mehmood","year":"2012","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.aca.2004.10.086","article-title":"Ensemble Methods and Data Augmentation by Noise Addition Applied to the Analysis of Spectroscopic Data","volume":"533","author":"Mevik","year":"2005","journal-title":"Anal. Chim. Acta"},{"key":"ref_46","unstructured":"Bjerrum, E.J., Glahder, M., and Skov, T. (2017). Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"108816","DOI":"10.1016\/j.foodcont.2022.108816","article-title":"Use of Convolutional Neural Network (CNN) Combined with FT-NIR Spectroscopy to Predict Food Adulteration: A Case Study on Coffee","volume":"135","author":"Moscetti","year":"2022","journal-title":"Food Control"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1002\/(SICI)1099-128X(199701)11:1<39::AID-CEM433>3.0.CO;2-S","article-title":"Prediction Intervals in Partial Least Squares","volume":"11","author":"Denham","year":"1997","journal-title":"J. Chemom."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1255\/jnirs.25","article-title":"A Review of Process near Infrared Spectroscopy: 1980\u20131994","volume":"1","author":"Workman","year":"1993","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101829","DOI":"10.1016\/j.ecoinf.2022.101829","article-title":"Detection of Citrus Black Spot Disease and Ripeness Level in Orange Fruit Using Learning-to-Augment Incorporated Deep Networks","volume":"71","author":"Momeny","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Naranjo-Torres, J., Mora, M., Hern\u00e1ndez-Garc\u00eda, R., Barrientos, R.J., Fredes, C., and Valenzuela, A. (2020). A Review of Convolutional Neural Network Applied to Fruit Image Processing. Appl. Sci., 10.","DOI":"10.3390\/app10103443"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e3004","DOI":"10.1002\/cem.3004","article-title":"Data Augmentation in Food Science: Synthesising Spectroscopic Data of Vegetable Oils for Performance Enhancement","volume":"32","author":"Georgouli","year":"2018","journal-title":"J. Chemom."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority over-Sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"118848","DOI":"10.1016\/j.fuel.2020.118848","article-title":"A New Method of Diesel Fuel Brands Identification: SMOTE Oversampling Combined with XGBoost Ensemble Learning","volume":"282","author":"Wang","year":"2020","journal-title":"Fuel"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bogner, C., K\u00fchnel, A., and Huwe, B. (2014, January 24\u201327). Predicting with Limited Data\u2014Increasing the Accuracy in Vis-Nir Diffuse Reflectance Spectroscopy by Smote. Proceedings of the Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077584"},{"key":"ref_56","unstructured":"Kumar, A., Goel, S., Sinha, N., and Bhardwaj, A. (2021, January 23\u201324). A Review on Unbalanced Data Classification. Proceedings of the International Joint Conference on Advances in Computational Intelligence: IJCACI 2021, Online Streaming."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative Adversarial Networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","article-title":"Generative Adversarial Networks: An Overview","volume":"35","author":"Creswell","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_59","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2015, January 8\u201313). Generative Adversarial Nets. Proceedings of the Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, QC, Canada."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"107208","DOI":"10.1016\/j.compag.2022.107208","article-title":"Generative Adversarial Networks (GANs) for Image Augmentation in Agriculture: A Systematic Review","volume":"200","author":"Lu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3487891","article-title":"Video Generative Adversarial Networks: A Review","volume":"55","author":"Aldausari","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","article-title":"A Survey of the Usages of Deep Learning for Natural Language Processing","volume":"32","author":"Otter","year":"2021","journal-title":"IEEE Trans. Neural. Netw. Learn. Syst."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"101308","DOI":"10.1016\/j.csl.2021.101308","article-title":"Generative Adversarial Networks for Speech Processing: A Review","volume":"72","author":"Wali","year":"2022","journal-title":"Comput. Speech Lang."},{"key":"ref_64","first-page":"557","article-title":"Deepfake: An Overview","volume":"Volume 203","author":"Chadha","year":"2021","journal-title":"Proceedings of the Second International Conference on Computing, Communications, and Cyber-Security"},{"key":"ref_65","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional Generative Adversarial Nets. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/LGRS.2019.2924059","article-title":"Semisupervised Hyperspectral Image Classification with Cluster-Based Conditional Generative Adversarial Net","volume":"17","author":"Zhao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 2\u20134). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Proceedings of the 4th International Conference on Learning Representations, ICLR 2016\u2014Conference Track Proceedings, San Juan, Puerto Rico."},{"key":"ref_68","first-page":"3581","article-title":"Semi-Supervised Learning with Deep Generative Models","volume":"4","author":"Kingma","year":"2014","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"ref_69","unstructured":"Odena, A. (2016). Semi-Supervised Learning with Generative Adversarial Networks. arXiv."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, S., Li, M., Zhou, M., and Chen, E. (November, January 31). Bidirectional Generative Adversarial Networks for Neural Machine Translation. Proceedings of the CoNLL 2018\u201422nd Conference on Computational Natural Language Learning, Brussels, Belgium.","DOI":"10.18653\/v1\/K18-1019"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"131047","DOI":"10.1016\/j.foodchem.2021.131047","article-title":"Near-Infrared Hyperspectral Imaging Technology Combined with Deep Convolutional Generative Adversarial Network to Predict Oil Content of Single Maize Kernel","volume":"370","author":"Zhang","year":"2022","journal-title":"Food Chem."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Yang, B., Chen, C., Chen, F., Chen, C., Tang, J., Gao, R., and Lv, X. (2021). Identification of Cumin and Fennel from Different Regions Based on Generative Adversarial Networks and near Infrared Spectroscopy. Spectrochim. Acta A Mol. Biomol. Spectrosc., 260.","DOI":"10.1016\/j.saa.2021.119956"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0304-4238(96)00946-6","article-title":"Viral Diseases Causing the Greatest Economic Losses to the Tomato Crop. I. The Tomato Spotted Wilt Virus\u2014A Review","volume":"67","author":"Nuez","year":"1996","journal-title":"Sci. Hortic."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Wang, D., Vinson, R., Holmes, M., Seibel, G., Bechar, A., Nof, S., Luo, Y., and Tao, Y. (August, January 29). Early Tomato Spotted Wilt Virus Detection Using Hyperspectral Imaging Technique and Outlier Removal Auxiliary Classifier Generative Adversarial Nets (OR-AC-GAN). Proceedings of the ASABE 2018 Annual International Meeting, Detroit, MI, USA.","DOI":"10.13031\/aim.201800660"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"138477","DOI":"10.1016\/j.scitotenv.2020.138477","article-title":"Classification of Pathogens by Raman Spectroscopy Combined with Generative Adversarial Networks","volume":"726","author":"Yu","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"122901","DOI":"10.1016\/j.talanta.2021.122901","article-title":"Raman Spectroscopy-Based Adversarial Network Combined with SVM for Detection of Foodborne Pathogenic Bacteria","volume":"237","author":"Du","year":"2022","journal-title":"Talanta"},{"key":"ref_77","unstructured":"Ouali, Y., Hudelot, C., and Tami, M. (2020). An Overview of Deep Semi-Supervised Learning. arXiv."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"108657","DOI":"10.1016\/j.ymssp.2021.108657","article-title":"A Semi-Supervised GAN Method for RUL Prediction Using Failure and Suspension Histories","volume":"168","author":"He","year":"2022","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_79","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X. (2016, January 5\u201310). Improved Techniques for Training GANs. Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016), Barcelona, Spain."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","article-title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","volume":"41","author":"Miyato","year":"2019","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"ref_81","first-page":"146","article-title":"Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery","volume":"Volume 10265","author":"Schlegl","year":"2017","journal-title":"Information Processing in Medical Imaging, Proceedings of the 25th International Conference, IPMI 2017, Boone, NC, USA, 25\u201330 June 2017"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/JBHI.2019.2922986","article-title":"Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks","volume":"24","author":"Yang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Madani, A., Moradi, M., Karargyris, A., and Syeda-Mahmood, T. (2018, January 4\u20137). Semi-Supervised Learning with Generative Adversarial Networks for Chest X-ray Classification with Ability of Data Domain Adaptation. Proceedings of the International Symposium on Biomedical Imaging, Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363749"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"27736","DOI":"10.1109\/ACCESS.2021.3058334","article-title":"An Imbalanced Fault Diagnosis Method for Rolling Bearing Based on Semi-Supervised Conditional Generative Adversarial Network with Spectral Normalization","volume":"9","author":"Xu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_85","unstructured":"Springenberg, J.T. (2015). Unsupervised and Semi-Supervised Learning with Categorical Generative Adversarial Networks. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2019.06.004","article-title":"Generalizing Semi-Supervised Generative Adversarial Networks to Regression Using Feature Contrasting","volume":"186","author":"Olmschenk","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_87","unstructured":"Kerdegari, H., Razaak, M., Argyriou, V., and Remagnino, P. (2019). Semi-Supervised GAN for Classification of Multispectral Imagery Acquired by UAVs. arXiv."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/LRA.2017.2774979","article-title":"WeedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming","volume":"3","author":"Sa","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Khan, S., Tufail, M., Khan, M.T., Khan, Z.A., Iqbal, J., and Alam, M. (2021). A Novel Semi-Supervised Framework for UAV Based Crop\/Weed Classification. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251008"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks","volume":"15","author":"Zhan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"He, Z., Liu, H., Wang, Y., and Hu, J. (2017). Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9101042"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8562\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:09:15Z","timestamp":1760130555000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/20\/8562"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,18]]},"references-count":91,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["s23208562"],"URL":"https:\/\/doi.org\/10.3390\/s23208562","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,18]]}}}