{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:57:29Z","timestamp":1783529849639,"version":"3.55.0"},"reference-count":64,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T00:00:00Z","timestamp":1673913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020","award":["871704"],"award-info":[{"award-number":["871704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Spectroscopy is a widely used technique that can contribute to food quality assessment in a simple and inexpensive way. Especially in grape production, the visible and near infrared (VNIR) and the short-wave infrared (SWIR) regions are of great interest, and they may be utilized for both fruit monitoring and quality control at all stages of maturity. The aim of this work was the quantitative estimation of the wine grape ripeness, for four different grape varieties, by using a highly accurate contact probe spectrometer that covers the entire VNIR\u2013SWIR spectrum (350\u20132500 nm). The four varieties under examination were Chardonnay, Malagouzia, Sauvignon-Blanc, and Syrah and all the samples were collected over the 2020 and 2021 harvest and pre-harvest phenological stages (corresponding to stages 81 through 89 of the BBCH scale) from the vineyard of Ktima Gerovassiliou located in Northern Greece. All measurements were performed in situ and a refractometer was used to measure the total soluble solids content (\u00b0Brix) of the grapes, providing the ground truth data. After the development of the grape spectra library, four different machine learning algorithms, namely Partial Least Squares regression (PLS), Random Forest regression, Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), coupled with several pre-treatment methods were applied for the prediction of the \u00b0Brix content from the VNIR\u2013SWIR hyperspectral data. The performance of the different models was evaluated using a cross-validation strategy with three metrics, namely the coefficient of the determination (R2), the root mean square error (RMSE), and the ratio of performance to interquartile distance (RPIQ). High accuracy was achieved for Malagouzia, Sauvignon-Blanc, and Syrah from the best models developed using the CNN learning algorithm (R2&gt;0.8, RPIQ\u22654), while a good fit was attained for the Chardonnay variety from SVR (R2=0.63, RMSE=2.10, RPIQ=2.24), proving that by using a portable spectrometer the in situ estimation of the wine grape maturity could be provided. The proposed methodology could be a valuable tool for wine producers making real-time decisions on harvest time and with a non-destructive way.<\/jats:p>","DOI":"10.3390\/s23031065","type":"journal-article","created":{"date-parts":[[2023,1,17]],"date-time":"2023-01-17T05:36:55Z","timestamp":1673933815000},"page":"1065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Estimation of Sugar Content in Wine Grapes via In Situ VNIR\u2013SWIR Point Spectroscopy Using Explainable Artificial Intelligence Techniques"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2693-1239","authenticated-orcid":false,"given":"Eleni","family":"Kalopesa","sequence":"first","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1190-5772","authenticated-orcid":false,"given":"Konstantinos","family":"Karyotis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"},{"name":"School of Science and Technology, International Hellenic University, 14th km Thessaloniki\u2014N. Moudania, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1502-3219","authenticated-orcid":false,"given":"Nikolaos","family":"Tziolas","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1904-9029","authenticated-orcid":false,"given":"Nikolaos","family":"Tsakiridis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1619-778X","authenticated-orcid":false,"given":"Nikiforos","family":"Samarinas","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8078-2601","authenticated-orcid":false,"given":"George","family":"Zalidis","sequence":"additional","affiliation":[{"name":"Laboratory of Remote Sensing, Spectroscopy, and GIS, School of Agriculture, Aristotle University of Thessaloniki, 57001 Thermi, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"key":"ref_1","unstructured":"(2021). World Food and Agriculture\u2014Statistical Yearbook 2021, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103479","DOI":"10.1016\/j.infrared.2020.103479","article-title":"Towards fruit maturity estimation using NIR spectroscopy","volume":"111","author":"Shah","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/0957126022000017981","article-title":"The Quality of Grapes and Wine in Relation to Geography: Notions of Terroir at Various Scales","volume":"13","author":"Vaudour","year":"2002","journal-title":"J. Wine Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1080\/10408398.2017.1355776","article-title":"Relationship between wine composition and temperature: Impact on Bordeaux wine typicity in the context of global warming\u2014Review","volume":"59","author":"Drappier","year":"2017","journal-title":"Crit. Rev. Food Sci. Nutr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"275","DOI":"10.20870\/oeno-one.2022.56.1.4857","article-title":"Grapevine row orientation, vintage and grape ripeness effect on anthocyanins, flavan-3-ols, flavonols and phenolic acids: I. Vitis vinifera L. cv. Syrah grapes","volume":"56","author":"Minnaar","year":"2022","journal-title":"OENO One"},{"key":"ref_6","unstructured":"Kader, A.A. (2002). Maturation and Maturity Indices. Postharvest Technology of Horticultural Crops, University of California."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Maicas, S. (2021). Advances in Wine Fermentation. Fermentation, 7.","DOI":"10.3390\/fermentation7030187"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Peng, J., Xie, W., Jiang, J., Zhao, Z., Zhou, F., and Liu, F. (2020). Fast Quantification of Honey Adulteration with Laser-Induced Breakdown Spectroscopy and Chemometric Methods. Foods, 9.","DOI":"10.3390\/foods9030341"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Karyotis, K., Angelopoulou, T., Tziolas, N., Palaiologou, E., Samarinas, N., and Zalidis, G. (2021). Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation. Land, 10.","DOI":"10.3390\/land10010063"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"248","DOI":"10.3136\/fstr.6.248","article-title":"Non-Destructive Techniques for Quality Evaluation of Intact Fruits and Vegetables","volume":"6","author":"Jha","year":"2000","journal-title":"Food Sci. Technol. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1023\/A:1015046908814","article-title":"Sugar Determination in Grapes Using NIR Technology","volume":"22","author":"Arazuri","year":"2001","journal-title":"Int. J. Infrared Millim. Waves"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1255\/jnirs.566","article-title":"Maturity, Variety and Origin Determination in White Grapes (Vitis Vinifera L.) Using near Infrared Reflectance Technology","volume":"13","author":"Arana","year":"2005","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1002\/jsfa.7053","article-title":"Rapid monitoring of grape withering using visible near-infrared spectroscopy","volume":"95","author":"Beghi","year":"2015","journal-title":"J. Sci. Food Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"256","DOI":"10.5344\/ajev.2010.10041","article-title":"Application of NIR-AOTF Spectroscopy to Monitor Aleatico Grape Dehydration for Passito Wine Production","volume":"62","author":"Bellincontro","year":"2011","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.jfoodeng.2010.06.016","article-title":"First steps towards the development of a non-destructive technique for the quality control of wine grapes during on-vine ripening and on arrival at the winery","volume":"101","year":"2010","journal-title":"J. Food Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.foodchem.2015.05.080","article-title":"A rapid qualitative and quantitative evaluation of grape berries at various stages of development using Fourier-transform infrared spectroscopy and multivariate data analysis","volume":"190","author":"Musingarabwi","year":"2016","journal-title":"Food Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ferrara, G., Marcotuli, V., Didonna, A., Stellacci, A.M., Palasciano, M., and Mazzeo, A. (2022). Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device. Horticulturae, 8.","DOI":"10.3390\/horticulturae8070613"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chariskou, C., Vrochidou, E., Daniels, A.J., and Kaburlasos, V.G. (2022). Variable Selection on Reflectance NIR Spectra for the Prediction of TSS in Intact Berries of Thompson Seedless Grapes. Agronomy, 12.","DOI":"10.3390\/agronomy12092113"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gomes, V., Mendes-Ferreira, A., and Melo-Pinto, P. (2021). Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries. Sensors, 21.","DOI":"10.3390\/s21103459"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.foodres.2008.11.008","article-title":"Shortwave-near infrared spectroscopy for determination of reducing sugar content during grape ripening, winemaking, and aging of white and red wines","volume":"42","author":"Morales","year":"2009","journal-title":"Food Res. Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1109\/TIM.2007.910098","article-title":"A Multipurpose Portable Instrument for Determining Ripeness in Wine Grapes Using NIR Spectroscopy","volume":"57","author":"Larrain","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vrochidou, E., Bazinas, C., Manios, M., Papakostas, G.A., Pachidis, T.P., and Kaburlasos, V.G. (2021). Machine Vision for Ripeness Estimation in Viticulture Automation. Horticulturae, 7.","DOI":"10.3390\/horticulturae7090282"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117","DOI":"10.5344\/ajev.2013.13024","article-title":"Wavelength Selection with a View to a Simplified Handheld Optical System to Estimate Grape Ripeness","volume":"65","author":"Giovenzana","year":"2013","journal-title":"Am. J. Enol. Vitic."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1255\/jnirs.679","article-title":"Analysis of Grapes and Wine by near Infrared Spectroscopy","volume":"14","author":"Cozzolino","year":"2006","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1080\/05704928.2014.966380","article-title":"A Review of the State of the Art, Limitations, and Perspectives of Infrared Spectroscopy for the Analysis of Wine Grapes, Must, and Grapevine Tissue","volume":"50","author":"Dambergs","year":"2014","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Power, A., Truong, V.K., Chapman, J., and Cozzolino, D. (2019). From the Laboratory to The Vineyard\u2014Evolution of The Measurement of Grape Composition using NIR Spectroscopy towards High-Throughput Analysis. High-Throughput, 8.","DOI":"10.3390\/ht8040021"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tiwari, P., Bhardwaj, P., Somin, S., Parr, W.V., Harrison, R., and Kulasiri, D. (2022). Understanding Quality of Pinot Noir Wine: Can Modelling and Machine Learning Pave the Way?. Foods, 11.","DOI":"10.3390\/foods11193072"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ranaweera, R.K.R., Capone, D.L., Bastian, S.E.P., Cozzolino, D., and Jeffery, D.W. (2021). A Review of Wine Authentication Using Spectroscopic Approaches in Combination with Chemometrics. Molecules, 26.","DOI":"10.3390\/molecules26144334"},{"key":"ref_29","first-page":"103069","article-title":"Using NDVI, climate data and machine learning to estimate yield in the Douro wine region","volume":"114","author":"Barriguinha","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106905","DOI":"10.1016\/j.compag.2022.106905","article-title":"Vineyard classification using OBIA on UAV-based RGB and multispectral data: A case study in different wine regions","volume":"196","author":"Matese","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102345","DOI":"10.1016\/j.foodpol.2022.102345","article-title":"Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications","volume":"112","author":"Resce","year":"2022","journal-title":"Food Policy"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1111\/j.1755-0238.1995.tb00085.x","article-title":"Growth Stages of the Grapevine: Phenological growth stages of the grapevine (Vitis vinifera L. ssp. vinifera)\u2014Codes and descriptions according to the extended BBCH scale","volume":"1","author":"Lorenz","year":"1995","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.trac.2009.07.007","article-title":"Review of the most common pre-processing techniques for near-infrared spectra","volume":"28","author":"Rinnan","year":"2009","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1111\/j.1365-2389.2009.01219.x","article-title":"Discriminating between organic matter in soil from grass and forest by near-infrared spectroscopy","volume":"61","author":"Ertlen","year":"2010","journal-title":"Eur. J. Soil Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.geoderma.2008.04.007","article-title":"Comparison of multivariate methods for inferential modeling of soil carbon using visible\/near-infrared spectra","volume":"146","author":"Vasques","year":"2008","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1002\/cem.1180020306","article-title":"PLS regression methods","volume":"2","year":"1988","journal-title":"J. Chemom."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_38","unstructured":"Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., and Vapnik, V. (1996, January 2\u20135). Support Vector Regression Machines. Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, CO, USA. NIPS\u201996."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"114208","DOI":"10.1016\/j.geoderma.2020.114208","article-title":"Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network","volume":"367","author":"Tsakiridis","year":"2020","journal-title":"Geoderma"},{"key":"ref_40","first-page":"1","article-title":"Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization","volume":"18","author":"Li","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1016\/j.trac.2010.05.006","article-title":"Critical review of chemometric indicators commonly used for assessing the quality of the prediction of soil attributes by NIR spectroscopy","volume":"29","author":"Palagos","year":"2010","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"066138","DOI":"10.1103\/PhysRevE.69.066138","article-title":"Estimating mutual information","volume":"69","author":"Kraskov","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/cem.2627","article-title":"Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)","volume":"28","author":"Eriksson","year":"2014","journal-title":"J. Chemom."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1093\/bioinformatics\/bty373","article-title":"The revival of the Gini importance?","volume":"34","author":"Nembrini","year":"2018","journal-title":"Bioinformatics"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_48","unstructured":"Covert, I.C., Lundberg, S., and Lee, S.I. (2020, January 6\u201312). Understanding Global Feature Contributions with Additive Importance Measures. Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada. NIPS\u201920."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Eatwell, J., Milgate, M., and Newman, P. (1989). Shapley Value. Game Theory, Palgrave Macmillan UK.","DOI":"10.1007\/978-1-349-20181-5"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Daniels, A., Opara, U., Poblete-Echeverr\u00eda, C., and Nieuwoudt, H. (2018). Novel approach for measuring sugar and acidity non-destructively in whole table grape bunches. Acta Hortic., 317\u2013324.","DOI":"10.17660\/ActaHortic.2018.1201.43"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1366\/000370203321535033","article-title":"Short-Wavelength Near-Infrared Spectra of Sucrose, Glucose, and Fructose with Respect to Sugar Concentration and Temperature","volume":"57","author":"Golic","year":"2003","journal-title":"Appl. Spectrosc."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Workman, J., and Weyer, L. (2012). Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy, CRC Press.","DOI":"10.1201\/b11894"},{"key":"ref_53","unstructured":"Osborne, B.G., Fearn, T., Hindle, P.H., and Hindle, P.T. (1993). Practical NIR Spectroscopy with Applications in Food and Beverage Analysis, Longman Scientific and Technical."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.biosystemseng.2010.02.002","article-title":"Assessment of the quality parameters in grapes using VIS\/NIR spectroscopy","volume":"105","author":"Kemps","year":"2010","journal-title":"Biosyst. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/10942912.2016.1144200","article-title":"Development of a multispectral imaging system for online quality assessment of pomegranate fruit","volume":"20","author":"Khodabakhshian","year":"2016","journal-title":"Int. J. Food Prop."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.jfoodeng.2006.10.016","article-title":"Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry","volume":"81","author":"ElMasry","year":"2007","journal-title":"J. Food Eng."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"123201","DOI":"10.1117\/1.2818812","article-title":"Development of a multispectral imaging prototype for real-time detection of apple fruit firmness","volume":"46","author":"Peng","year":"2007","journal-title":"Opt. Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.optlastec.2018.04.017","article-title":"Multispectral imaging for predicting sugar content of \u2018Fuji\u2019 apples","volume":"106","author":"Tang","year":"2018","journal-title":"Opt. Laser Technol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Silva, R., Gomes, V., Mendes-Faia, A., and Melo-Pinto, P. (2018). Using Support Vector Regression and Hyperspectral Imaging for the Prediction of Oenological Parameters on Different Vintages and Varieties of Wine Grape Berries. Remote Sens., 10.","DOI":"10.3390\/rs10020312"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1007\/s11119-022-09913-3","article-title":"Intelligent robots for fruit harvesting: Recent developments and future challenges","volume":"23","author":"Zhou","year":"2022","journal-title":"Precis. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Santos, C.H., and Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20.","DOI":"10.3390\/s20092672"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.biosystemseng.2019.03.007","article-title":"Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms","volume":"181","author":"Williams","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"107889","DOI":"10.1016\/j.asoc.2021.107889","article-title":"A review of different dimensionality reduction methods for the prediction of sugar content from hyperspectral images of wine grape berries","volume":"113","author":"Silva","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"114967","DOI":"10.1016\/j.geoderma.2021.114967","article-title":"Using autoencoders to compress soil VNIR\u2013SWIR spectra for more robust prediction of soil properties","volume":"393","author":"Tsimpouris","year":"2021","journal-title":"Geoderma"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1065\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:08:12Z","timestamp":1760119692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,17]]},"references-count":64,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031065"],"URL":"https:\/\/doi.org\/10.3390\/s23031065","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,17]]}}}