Papers

Digital Agriculture, Food and Wine

Peer-reviewed publications produced by the Digital Agriculture, Food and Wine group.

2022-2026

    1. Gonzalez Viejo, C., Villarreal-Lara, R., Mayorga-Martinez, A.A., Harris, N., and Fuentes, S. 2026. Brewing for Beyond Earth: Assessing How Simulated Space Environment Alters Beer Perception, with AI-Driven Sensory Prediction from E-Nose Data. Future Foods, p.101009.
    2. Rodriguez-Velazco Y.G., Mayorga-Martinez, A., Clorio-Carrillo, J.A., Lozano-De la Garza, B., Castill-Alvarez, A., Gonzalez Viejo, C., Fuentes, S., Villarreal-Lara, R., and Hernandez-Brenes, C. 2026. Drivers of consumer liking in top- and bottom-fermented beers: Integrating sensometric and chemometric insights. Food Research International, 119037.
    3. Pointner, T., Gonzalez Viejo, C., Fuentes, S., and Pignitter, M. 2026. Machine Learning-Based Prediction of Cold-Pressed Oil Storage Time Using NIR and Electronic-Nose Data. Future Foods, p.100967.
    4. Mayorga-Martinez, A.A., Gonzalez Viejo, C., Clorio-Carrillo, J.A., Obispo-Fortunato, D.J., Villarreal-Lara, R., Torrico, D.D., Patino-Gonzalez, V., Hernandez-Brenes, C., and Fuentes, S. 2026. Electronic nose and machine learning for rapid, sustainable detection of avocado oil adulteration. Journal of Agriculture and Food Research.
    5. Fuentes, S., Harris, N., and Gonzalez Viejo, C. 2026. Digital Agricultural Strategies and AI for Astronaut Food Self-Sufficiency in Space Exploration. Conference: IAF/IAA Space Life Sciences Symposium, Held at the 76th International Astronautical Congress (IAC 2025). DOI: 10.52202/083074-0042
    6. Gonzalez Viejo, C., Harris, N., Mayorga-Martinez, A.A., and Fuentes, S. 2026. Brews in Space: Modelling Beer Acceptability in Microgravity Environments Using Digital Technologies and AI. Conference: IAF/IAA Space Life Sciences Symposium, Held at the 76th International Astronautical Congress (IAC 2025). https://doi.org/10.52202/083074-0092

    1. Fuentes, S., Gonzalez Viejo, C., and Tongson, E. Unleashing the power of Artificial Intelligence for Viticulture and Oenology on Earth and Space. 2025. IVES Conference Series.
    2. Shi, L., Wu, H., Ghafoor, K., Gonzalez Viejo, C., Fuentes, S., Ahmadi, F., and Suleria, H.A.S. 2025. Impacts of roasting intensity and cultivar on date seed beverage quality traits and volatile compounds using digital technologies. Foods, 14(22), p.3902.
    3. Shahid, A., Fuentes, S., Gonzalez Viejo, C., Widdicombe, B. and Unnithan, R.R., 2025. Automated Assessment of Green Infrastructure Using E-nose, Integrated Visible-Thermal Cameras and Computer Vision Algorithms. Sensors, 25(22), p.6812.
    4. Luke L. Fountain, Matthew Gilliham, Chiara Amitrano, Nafiou Arouna, Richard J. Barker, Maik Bohmer, Markus Braun, Nicolas J.B. Brereton, Rebecca L. Brocato, Jess M. Bunchek, Emma Canaday, Nicol Caplin, Paola Castaño, Mélanie Decourteix, Marta Del Bianco, Veronica De Micco, Colleen Doherty, Christine Escobar, Michel F. Franke, Sigfredo Fuentes, Simon Gilroy, Karl H. Hasenstein, Jens Hauslage, Raul Herranz, Anjali Iyer-Pazcussi, Dylan Shun Izuma, Kirima Junya, John Z. Kiss, Valérie Legué, James P.B. Lloyd, Gioia D. Massa, Massimo E. Maffei, Alexander D. Meyers, Imara Y. Perera, Lucie Poulet, Suruchi Roychoudry, Giovanni Sena, Dorothy Shippen, Jared Stoochnoff, Hideyuki Takahashi, Sarah E. Wyatt, and Elison B. Blancaflor. 2025. Expanding frontiers: harnessing plant biology for space exploration and planetary sustainability. New Phythologist, 249(2), pp.656-669.
    5. Gonzalez Viejo, C., Mayorga-Martinez, A.A., Harris, N., Villarreal-Lara, R., and Fuentes, S. 2025. Spaceward Senses: Examining Retronasal Aroma and Mouthfeel Perception in Simulated Space-Microgravity Environments. npj Science of Food, 9(1), p.202. UoMLogoTecLogo2
    6. Perez-Perez, C.A., Gonzalez Viejo, C., Fuentes, S., and Valiente-Banuet, J.I. 2025. Vineyard Proximal Sensing Using Multispectral Imaging to Evaluate Grape Ripening and Quality Traits Using Artificial Neural Networks Modeling. J. Agriculture and Food Research p.102252UoMLogoTecLogo2
    7. Shi, L., Sejpal, M., Ghafoor, K., Gonzalez Viejo, C., Fuentes, S., Ahmadi, F., and Suleria, H.A.R. 2025. Ripening Stage and Phenolic Composition Characterization of Fruit from Different Date (Phoenix dactylifera L.) Cultivars in Australia Food Science &  Nutrition. 13(5), p.e70221
    8. Shi, L., Ghafoor, K., Gonzalez Viejo, C., Fuentes, S., Ahmadi, F., and Suleria, H.A.R. 2025.  Impact of roasting temperature on antioxidant activities and characterization of polyphenols in date seed beverages from different cultivars. Journal of Food Science. 90(5), p.e70242
    9. Marin-Obispo, L.M., Mayorga-Martinez, A.A., Obispo-Fortunato, D.J., Clorio-Carrillo, J.A., Gonzalez Viejo, C., Fuentes, S., Schwinghamer, T., Patiño-Gonzalez, V., and Hernandez-Brenes, C. 2025. Fatty acids and fatty alcohol esters as novel markers of authenticity and extraction method of commercial avocado oil. Food Chemistry X, p. 102451 UoMLogoTecLogo2
    10. Clorio-Carrillo, J.A., Perez-Carrillo, E., Villarreal-Lara, R., Gonzalez Viejo, C., Heredia Olea, E., De Anda Lobo, I.C., Fuentes, S., Ramos-Parra, P.A., and Hernandez-Brenes, C. 2025. Chemometric Mapping of Beer Styles: Integration of Hordenine into the Beer Composition Fingerprint. Food Chemistry, 478, p.143643 UoMLogoTecLogo2
    11. Ashfaq, W., Kaleem, M., Brodie, G., Fuentes, S., Pang, A., and Gupta, D. 2025. Silicon alleviates drought stress by up-regulating physiological and biochemical responses in two contrasting bread wheat cultivars. Cereal Research Communications, pp.1-13.
    12. Harris, N., Gonzalez Viejo, C., Zhang, J., Pang, A., Hernandez-Brenes, C., and Fuentes, S. 2025. Enhancing Beer Authentication, Quality and Control Assessment Using Non-Invasive Spectroscopy Through Bottle and Machine Learning Modeling. Journal of Food Science, e17670.UoMLogoTecLogo2
    13. Rodriguez-Sanchez, D., Marin-Obispo, L.M.,Velazquez-Garza, F., Garza-Aguilar S., Gonzalez Viejo, C., Fuentes, S., Colin-Oviedo, A., Diaz de la Garza, R., and Hernandez-Brenes, C. 2024. Joint Metabolites for Avocado Oil Identity: Fatty Acid Profiles and Odd-Chain Hydroxylated Lipids as Unique Derivatives. Journal of Agricultural and Food Chemistry, 73(2), p. 1529–1541UoMLogoTecLogo2

    1. Gonzalez Viejo, C., Hernandez-Brenes, C., and Fuentes, S. 2024. Editorial: Beer -From Tradition to Innovation. Frontiers in Nutrition, 11, p.1536519. UoMLogoTecLogo2
    2. Gonzalez Viejo, C., Torrico, D.D., and Fuentes, S. 2024.  Editorial: Advances in Sensory Evaluation of Foods. Frontiers in Food Science and Technology, 4, p.1534473. UoMLogoTecLogo2
    3. Fuentes, S. 2024. Advances in Methods and Protocols in Viticulture, Pomology, and Soft Fruits. Frontiers in Horticulture, 3, p.1504703. UoMLogoTecLogo2
    4. Hernandez-Brenes, C., Rodriguez-Sanchez, D.G., Villarreal-Lara, R., Gonzalez Viejo, C., and Clorio-Carrillo, J.A. 2024. Impact of Teaching Multivariate Modeling with Digital Tools on the Development Level of Experimental Data Analysis and Interpretation Skills. Proceedings of the 22nd LACCEI international Multi-conference for Engineering, Education and Technology. UoMLogoTecLogo2
    5. Gonzalez Viejo, C., Harris, N., Tongson, E., and Fuentes, S. 2024. Exploring Consumer Acceptability of Leafy Greens in Earth and Space Immersive Environments Using Biometrics. npj Science of Food, 8(1), p.81 UoMLogoTecLogo2
    6. Li, X, Hastie, M., Warner, R.D., Hewitt, R.J.E., D'Souza, D.N., Gonzalez Viejo, C., Fuentes, S., Ha, M., and Dunshea, F.R. 2024. Consumer eating quality and physicochemical traits of pork Longissimus and Semimembranosus differed between genetic lines. Meat Science. 218, 109631UoMLogoTecLogo2
    7. Wu, H., Gonzalez Viejo, C., Fuentes, S., Dunshea, F.R., and Suleria, H.A. 2024. Assessing the influence of spontaneous fermentation on consumer emotional responses to roasted arabica coffee in a biometric approach.  Food Research International. 195, p.114973 UoMLogoTecLogo2
    8. López-Olivari, R., Poblete-Echeverría, C., Fuentes, S. and Mora-Sanhueza, R. (2024). Effect of different irrigation levels on soil temperature and heat storage in a drip-irrigated potato crop. Acta Hortic. 1409, 67-72 doi.org/10.17660/ActaHortic.2024.1409.10
    9. Fuentes, S., Ortega Farias, S., Carrasco-Benavides, M., Tongson, E., and Gonzalez Viejo, C. 2024. Actual evapotranspiration and Energy Balance Estimation from Vineyards using micro-meteorological data and machine learning modelling. Agricultural Water Management. 297, p. 108834.UoMLogoTecLogo2
    10. Wang, W., Taylor, A., Tongson, E., Edwards J., Vaghefi, N., Ades, P.K., Crous, P.W., Taylor, P. 2024. Identification and pathogenicity of Colletotrichum species associated with twig dieback of citrus in Western Australia. Plant Pathology, 73(5), pp. 1194-1212.
    11. Ramos-Parra, P.A., De Anda-Lobo, I.C., Gonzalez Viejo, C., Villarreal-Lara R., Clorio-Carrillo, J.A., Marin-Obispo, L.M., Obispo-Fortunato, D.J., Escobedo-Avellaneda, Z., Fuentes, S., Pere-Carrillo, E. and Hernande-Brenes, C. 2024. Consumer Insights into the At-Home Liking of Commercial Beers: Integrating Nonvolatile and Volatile Flavor Chemometrics. Food Science & Nutrition. 2(6), pp. 4063-4075 UoMLogoTecLogo2
    12. Shi, L., Liu, Z., Gonzalez Viejo, C., Ahmadi, F., Dunshea, F.R., and Suleria, H. 2024. Comparison of phenolic composition in Australian-grown date fruit (Phoenix dactylifera L.) seeds from different varieties and ripening stages. Food Research International, 181, p.114096.
    13. Carrasco-Benavides, M.,  Espinoz, S., Umemura, K., Ortega-Farias, S., Baffico-Hernande, A., Neira Roman, J., Avila-Sanchez, C., and Fuentes, S. Evaluation of Thermal-Based Physiological Indicators for Determining Water Stress Thresholds in Drip- Irrigated 'Regina' Cherry Trees. Irrigation Science, 42(3), pp.445-459UoMLogoTecLogo2
    14. Tavan, M., Wee, B., Fuentes, S., Pang, A., Brodie, G., Gonzalez Viejo, C., and Gupta, D. 2024. Biofortification of kale microgreens with selenate-selenium using two delivery methods: Selenium-rich soilless medium and foliar application. Scientia Horticulturae, 323, 112522
    15. Gonzalez Viejo, C., Harris, N., and Fuentes, S. 2024. Assessment of Changes in Sensory Perception, Biometrics and Emotional Response for Space Exploration by Simulating Microgravity Positions. Food Research International, 175, p.113827.UoMLogoTecLogo2
    16. Ashfaq, W., Brodie, G., Fuentes, S., Pang, A. and Gupta, D., 2024. Silicon improves root system and canopy physiology in wheat under drought stress. Plant and Soil, 502(1), pp.279-296.
    17. Gonzalez Viejo, C., Harris, N., Barnes, C., Tongson, E., Hernandez-Brenes, C., Valiente-Banuet, J. and Fuentes, S. 2024. Non-invasive wine authentication method using near-infrared spectroscopy through the bottle. Acta Hortic. 1387, 121-126. doi.org/10.17660/ActaHortic.2024.1387.16 UoMLogoTecLogo2
    18. Fuentes, S., Tongson, E., Hernandez-Brenes, C., Valiente-Banuet, J., Villarreal-Lara, R., De Anda-Lobo, I., Harris, N., Dutton, J., Mattioli, F. and Gonzalez Viejo, C. 2024. Perception and acceptability of artificial intelligence applications in viticulture and wine by consumers from Mexico and Australia. Acta Hortic. 1387, 325-332 doi.org/10.17660/ActaHortic.2024.1387.46UoMLogoTecLogo2
    19. Wu, H, Gonzalez Viejo, C., Fuentes, S., Dunshea, F.R, and Suleria H. 2024. Evaluation of spontaneous fermentation impact on the physicochemical properties and sensory profile of green and roasted arabica coffee by digital technologies. Food Research International, 176: 113800.UoMLogoTecLogo2

    1. Harris, N., Gonzalez Viejo, C., Barnes, C., Pang, A., and Fuentes, S. 2023. Wine Quality Assessment for Shiraz Vertical Vintages based on Digital Technologies and Machine Learning Modelling. Food Bioscience, 56 p.103354 UoMLogoTecLogo2
    2. Biju, S., Fuentes, S., and Gupta, D. 2023. Novel insights into the mechanism(s) of silicon-induced drought stress tolerance in lentil plants revealed by RNA sequencing analysis. BMC Plant Biology, 23(1), p.498.
    3. Gonzalez Viejo, C., Torrico, D.D. and Fuentes, S. 2023. Novel Contactless Sensors for Food, Beverage and Packaging Evaluation. Editorial. Sensors, 23(19), p.8082UoMLogoTecLogo2
    4. Fuentes, S., Tongson, E., Gonzalez Viejo, C. 2023. New Developments and Opportunities for AI in Viticulture, Pomology and Soft Fruit Research: A Mini-Review and Invitation to Contribution Articles. Frontiers Horticulture, 2, p.1282615. UoMLogoTecLogo2
    5. Aznan, A., Gonzalez Viejo, C., Pang, A. and Fuentes, S. 2023. Review of Technology Advances to Assess Rice Quality Traits and Consumer Perception. J. Food Research International, 172, 113015. UoMLogoTecLogo2
    6. Mora-Poblete, F., Heidari, P., and Fuentes, S. 2023. Editorial: Integrating Advanced High-throughput Technologies to Improve Plant Resilience to Environmental Challenges. Frontiers in Plant Science, 14, p.121869. UoMLogoTecLogo2
    7. Biju, S., Fuentes, S. and Gupta, D., 2023. Regulatory role of silicon on photosynthesis, gas-exchange and yield related traits of drought-stressed lentil plants. Silicon, 15(14), pp.5981-5996.
    8. Cao, X., Wu, H., Gonzalez Viejo, C., and Suleria, H. 2023. Effects of Postharvest Processing on Aroma Formation in Roasted Coffee - A Review. International Journal of Food Science & Technology. 58(3) 1007-1027.
    9. Gonzalez Viejo, C., Hernandez-Brenes, C., Villarreal-Lara, R., De Anda-Lobo, I., Ramos-Parra, P.A., Perez-Carrillo, E., Clorio-Carrillo, J.A., Tongson, E., and Fuentes, S. 2023. Effects of Different Beer Compounds on Biometrically Assessed Emotional Responses in Consumers. Fermentation, 9(3), 269. UoMLogoTecLogo2
    10. Wu, H., Gonzalez Viejo, C., Fuentes, S., Dunshea, F.R., and Suleria, H.A.R. 2023. The impact of wet fermentation on coffee quality traits and volatile compounds using digital technologies.  Fermentation. 9(1), p.68.
    11. Harris, N., Gonzalez Viejo, C., Barnes, C., and Fuentes, S. 2023. Non-invasive digital technologies to assess wine quality traits and provenance through the bottle. Fermentation, 9(1). p.10
    12. Shin, M-Y., Gonzalez Viejo, C., Tongson, E., Wieche, T., Taylor, P.W.J., and Fuentes, S. 2023. Early detection of Verticillium wilt of potatoes using near-infrared spectroscopy and machine learning modelling. Computers and Electronics in Agriculture, 204. p.107567

    1. Aznan, A., Gonzalez Viejo, C., Pang, A., and Fuentes, S. 2022. Rapid Detection of Rice Adulteration using a Low-Cost  Electronic Nose and Machine Learning Modelling. Engineering Proceedings, 27(1), p. 1.
    2. Ashfaq, W., Brodie, G., Fuentes, S., and Gupta, D. 2022. Infrared thermal imaging and morpho-physiological indices used for wheat genotypes screening under drought and heat stress. Plants. 11(23), p. 3269.
    3. Aznan, A., Gonzalez Viejo, C., Pang, A., and Fuentes, S. 2022. Rapid Detection of Fraudulent Rice Using Low-Cost Digital Sensing Devices and Machine Learning. Sensors, 22(22) p. 8655.
    4. Feng, H., Gonzalez Viejo, C., Vaghefi, N., Taylor, P.W.J., Tongson, E., and Fuentes, S. 2022. Early detection of Fusarium oxysporum infection of processing tomatoes (Solanum lycopersicum) and pathogen-soil interactions using a low-cost portable electronic nose and machine learning modelling. Sensors, 22(22), p. 8645
    5. Ho Dac, H., Gonzalez Viejo, C., Lipovetzky, N., Tongson, E., Dunshea, F.R., and Fuentes, S. 2022. Livestock Identification using Deep Learning for Traceability. Sensors, 22(21), 8256.
    6. Gonzalez Viejo, C., Harris, N., and Fuentes, S. 2022. Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling. Fermentation, 8(10), p.516.  Selected as Journal Issue Cover
    7. Fuentes, S., and Chang, J. 2022. Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture. Sensors. 22(20), 7898.
    8. Fuentes, S., Gonzalez Viejo, C., Tongson, E., Dunshea, F.R., Dac, H.H. and Lipovetzky, N., 2022. Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems. Journal of Agriculture and Food Research, 10, p.100388.
    9. Mehta, A., Serventi, L., Kumar, L., Gonzalez Viejo, C., Fuentes, S., and Torrico, D.D. 2022. Influence of expectations and emotions raised by packaging characteristics on orange juice acceptability and choice. Food Packaging and Shelf Life, 33, p.100926.
    10. Carrasco-Benavides, M., Gonzalez Viejo, C., Tongson, E., Baffico-Hernandez, A., Avila-Sanchez, C., Mora, M., and Fuentes S. 2022.  Water status estimation of cherry trees using infrared thermal imagery coupled with supervised machine learning modelling. Computers and Electronics in Agriculture. 200, 107256
    11. Ashfaq, W., Fuentes, S., Brodie, G. and Gupta, D., 2022. The role of silicon in regulating physiological and biochemical mechanisms of contrasting bread wheat cultivars under terminal drought and heat stress environments. Frontiers in Plant Science, 13, p.955490.
    12. Lopez-Olivari, R.A., Fuentes, S., Poblete-Echeverria, C., Quintulen, V., and Medin, L. 2022. Site-specific evaluation of canopy resistance models for estimating evapotranspiration over a drip-irrigated potato crop in southern Chile under water-limited conditions. Water. 14(13), p. 2041
    13. Rasekh, M., Karami, H., Fuentes, S., Kaveh, M., Rusinek, R., and Gankarz, M. 2022. Preliminary study on Non-Destructive Sorting Techniques for Pepper (Capsicum annuum L.) Using Odor Parameter. LWT. p.113667
    14. Gonzalez Viejo, C., Tongson, E., and Fuentes, S. 2022. Novel Method to Conduct Remote Sensory Sessions and Biometrics During Isolation. Biology and Life Sciences Forum, 6(1) 88.
    15. Fuentes S., Gonzalez Viejo C., Tongson E., Dunshea F.R. 2022. The Livestock Farming Digital Transformation: Implementation of new and emerging technologies using artificial intelligence. Animal Health Research Reviews, 1-13. https://doi.org/10.1017/s1466252321000177
    16. Gonzalez Viejo, C., Fuentes, S., De Anda-Lobo, I.C., and Hernandez-Brenes, C. 2022. Remote sensory assessment of beer quality based on visual perception of foamability and biometrics compared to standard emotional responses from affective images. Food Research International. 156. 111341.
    17. Gonzalez Viejo, C., and Fuentes, S. 2022. Digital detection of olive oil rancidity levels and aroma profiles using near-infrared spectroscopy, a low-cost electronic nose and machine learning modelling. Chemosensors. 10(5), p. 159.
    18. Aznan, A., Gonzalez Viejo, C., Pang, A., and Fuentes, S. Rapid Assessment of Rice Quality Traits using a Low-Cost Digital Technologies. Foods. 11(9), 1181.
    19. Fuentes, S. 2022. Implementation of Artificial Intelligence in Food Science, Food Quality, and Consumer Preference Assessment. Foods. 11(9), 1192.
    20. Gupta, M., Gonzalez Viejo, C., Fuentes, S., Torrico, D.D., Saturno, P.C., Gras S.L., Dunshea, F.R., and Cottrell, J.J. 2022. Digital technologies to assess yoghurt quality traits and consumers acceptability. J. Science of Food and Agriculture, 102(13), pp.5642-5652. https://doi.org/10.1002/jsfa.11911
    21. Fuentes, S., Summerson, V., Tongson, E., and Gonzalez Viejo, C. 2022. Novel Digital Technologies to Assess Smoke Taint in Wine Using Non-Invasive Chemical Fingerprinting, a Low-Cost Electronic Nose, and Artificial Intelligence. Biology and Life Sciences Forum. 6(1), 56.
    22. Gonzalez Viejo, C, Fuentes, S. 2022. Rapid Method for Faults Detection in Beer Using a Low-Cost Electronic Nose and Machine Learning Modelling. Biology and Life Sciences Forum. 6(1), 46
    23. Gonzalez Viejo C., and Fuentes S. 2022. Digital Assessment and Classification of Wine Faults Using a Low-cost Electronic nose, near-infrared spectroscopy and Machine Learning Modelling. Sensors, 22(6) 2303.
    24. Gonzalez Viejo C., and Fuentes S. 2022. Editorial: Special Issue “Implementation of Digital Technologies on Beverage Fermentation”. Fermentation. 8(3) 127.
    25. Liu C., Sharma C., Xu Q., Gonzalez Viejo C., Fuentes S., and Torrico D. 2022. Influence of Label Design and Country of Origin Information in Wines on Consumers’ Visual, Sensory, and Emotional Responses. Sensors. 22(6) 2158 https://doi.org/10.3390/s22062158
    26. Carrasco-Benavides M., Ortega-Farias S., Gil P.M., Knopp D., Morales-Salinas L., Lagos O., De La Fuente D., Lopez-Olivari R., Fuentes S. 2022. Assessment of vineyard water footprint by using ancillary data and EEFlux satellite images. Examples in the Chilean central zone. Science of The Total Environment. 811, p.152452. doi.org/10.1016/j.scitotenv.2021.152452 
    27. Summerson, V., Gonzalez Viejo C., and Fuentes, S. 2022. Non-destructive methods for assessing smoke-derived compounds. IVES Technical Reviews Vine and Wine. March 2022. https://doi.org/10.20870/IVES-TR.2022.5407

2017-2021

    1. Fuentes S., Gonzalez Viejo C., Torrico D.D., Dunshea F.R. 2021. Digital integration and automated assessment of eye-tracking and emotional response data using the BioSensory App to maximise packaging label analysis. Sensors. 21(22), p.7641. doi.org/10.3390/s21227641
    2. Fuentes S., Gonzalez Viejo C., Summerson V., Hall C., Tang Y., Tongson E. 2021. Berry cell vitality assessment and the effect on wine sensory traits based on chemical fingerprinting, canopy architecture and machine learning modelling. Sensors. 21(21), p.7312. doi.org/10.3390/s21217312
    3. Fuentes S., Gonzalez Viejo C., Tongson E., Lipovetzky N., Dunshea F.R. 2021. Biometric and physiological responses from dairy cows measured by visible remote sensing are good predictors of milk productivity and quality through artificial intelligence. Sensors. 21(20), p.6844. doi.org/10.3390/s21206844
    4. Aznan, A., Gonzalez Viejo, C., Pang, A., & Fuentes, S. 2021. Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies. Sensors, 21(19), 6354. doi.org/10.3390/s21196354
    5. Fuentes, S., Tongson, E., Unnithan, R. R., & Gonzalez Viejo, C. 2021. Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modelling. Sensors, 21(17), 5948. doi.org/10.3390/s21175948
    6. Fuentes, S. and Tongson, E.J., 2021. Editorial Special Issue: Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems. Sensors, 21(19), p.6383. doi.org/10.3390/s21196383
    7. Summerson V., Gonzalez Viejo C., Pang A., Torrico D.D., Fuentes S. 2021. Assessment of volatile aromatic compounds in smoke tainted Cabernet Sauvignon wines using a low-cost e-nose and machine learning modelling. Molecules. 26(16), p.5108. doi.org/10.3390/molecules26165108
    8. Kerr E.D., Caboche C.H., Pegg C., Phung T., Gonzalez Viejo C., Fuentes S., Howes M.T., Howell K., Schultz B.L. 2021. The post-transitional modification landscape of commercial beers. Scientific Reports. 11, 15890. doi.org/10.1038/s41598-021-95036-0. Top 100 cell and molecular biology Scientific Reports papers in 2021.
    9. Gonzalez Viejo C., Fuentes S., Hernandez C. 2021. Smart detection of faults in beers using near-infrared spectroscopy, a low-cost electronic nose and artificial intelligence. Fermentation. 7(3), p.117 doi.org/10.3390/fermentation7030117 Recognition as Outstanding Publication and Editors Choice Article.
    10. Summerson V., Gonzaes Viejo C., Torrico D.D., Pang A., Fuentes S. 2021. Digital smoke taint detection in Pinot Grigio using an e-nose and machine learning algorithms following treatment with Activated carbon and cleaving enzyme. Fermentation. 7(3), p.119. doi.org/10.3390/fermentation7030119 Recognition as Outstanding Publication and Editors Choice Article.
    11. Park. S., Ryu D., Fuentes S., Chung H., O’Connell M., Kim J. Dependence of CWSIBased Plant Water Stress Estimation with Diurnal Acquisition Times in a Nectarine Orchard. 2021.  Remote Sensing. 13, 2775. doi.org/10.3390/rs13142775
    12. Jorquera-Chavez, M., Fuentes, S., Dunshea, F. R., Warner, R. D., Poblete, T., Unnithan, R. R., ... & Jongman, E. C. 2021. Using imagery and computer vision as remote monitoring methods for early detection of respiratory disease in pigs. Computers and Electronics in Agriculture, 187, 106283. doi.org/10.1016/j.compag.2021.106283
    13. Cameron W., Petrie P.R., Barlow E.W., Howell K., Patrick C.J. Fuentes S. 2021. A comparison of the effect of temperature on grapevine phenology between vineyard groups. Oeno One, 55(2), pp.301-320 doi.org/10.20870/oeno-one.2021.55.2.4599
    14. Biju S., Fuentes S., Gupta D. 2021. Silicon modulates nitro-oxidative homeostasis along with the antioxidant metabolism to promote drought stress tolerance in lentil plants. Physiologia Plantarum, 172(2), pp.1382-1398 doi.org/10.1111/ppl.13437
    15. Fuentes S., Tongson E., Gonzalez Viejo C. 2021. Novel digital technologies implemented in sensory science and consumer perception. Current Opinion in Food Science. 41: 99-106. doi.org/10.1016/j.cofs.2021.03.014
    16. Park S., Ryu D., Fuentes S., Chung H., O’Connell M. and Kim J. 2021. Mapping very high-resolution evapotranspiration from an unmanned aerial vehicle (UAV) imagery. International Journal of Geo-Information. 10(4), 211 doi.org/10.3390/ijgi10040211
    17. Gonzalez Viejo C., Tongson E., Fuentes S. 2021.  Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity. Sensors. 21(6), 2016; doi.org/10.3390/s21062016. Recognition as Outstanding Publication and Editors Choice Article.
    18. Gonzalez Viejo, C., Zhang, H., Khamly, A., Xing, Y., & Fuentes, S. 2021. Coffee Label Assessment Using Sensory and Biometric Analysis of Self-Isolating Panellists through Videoconference. Beverages, 7(1), p. 5. doi.org/10.3390/beverages7010005 Recognition as Outstanding Publication and Editors Choice Article.
    19. Cameron, W., Petrie, P. R., Barlow, E. W. R., Patrick, C. J., Howell, K., & Fuentes, S. 2021. Is advancement of grapevine maturity explained by an increase in the rate of ripening or advancement of veraison? Australian Journal of Grape and Wine Research, 27(3), pp.334-347 doi.org/10.1111/ajgw.12488
    20. Summerson, V., Gonzalez Viejo, C., Pang, A., Torrico, D. D., & Fuentes, S. 2021. Review of the Effects of Grapevine Smoke Exposure and Technologies to Assess Smoke Contamination and Taint in Grapes and Wine. Beverages, 7(1), 7. doi.org/10.3390/beverages7010007 Recognition as Outstanding Publication and Editors Choice Article.
    21. Tavan, M., Wee, B., Brodie, G., Fuentes, S., Pang, A., & Gupta, D. 2021. Optimizing Sensor-Based Irrigation Management in a Soilless Vertical Farm for Growing Microgreens. Frontiers Sustainability for Food Systems. 4: 622720. doi.org/10.3389/fsufs.2020.622720
    22. Fuentes, S., Tongson, E., & Gonzalez Viejo, C. 2021. Urban Green Infrastructure Monitoring Using Remote Sensing from Integrated Visible and Thermal Infrared Cameras Mounted on a Moving Vehicle. Sensors, 21(1), 295. doi.org/10.3390/s21010295
    23. Torrico D.D., Sharma C., Dong W., Fuentes S., Gonzalez Viejo C., and Dunshea F.R. 2021. Virtual reality environments on the sensory acceptability and emotional responses of no-and full-sugar chocolate."  Food Science and Technology Journal. LWT 137: 110383. doi.org/10.1016/j.lwt.2020.110383

    1. Nouraki, A., Akhavan, S., Rezaei, Y., & Fuentes, S. 2020. Assessment of sunflower water stress using infrared thermometry and computer vision analysis. Water Supply, 21(3), pp.1228-1242 doi.org/10.2166/ws.2020.382
    2. Gonzalez Viejo, C., & Fuentes, S. 2020. Low-Cost Methods to Assess Beer Quality Using Artificial Intelligence Involving Robotics, an Electronic Nose, and Machine Learning. Fermentation, 6(4), p. 104. doi.org/10.3390/fermentation6040104
    3. Fuentes, S., Gonzalez Viejo, C., Chauhan, S. S., Joy, A., Tongson, E., & Dunshea, F. R. 2020. Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modelling Using Integrated Visible/Infrared Thermal Cameras. Sensors, 20(21), 6334. doi.org/10.3390/s20216334
    4. Vasiliki S., Gonzalez Viejo C., Torrico D.D., Pang A., and Fuentes S.. 2020. Detection of smoke-derived compounds from bushfires in Cabernet-Sauvignon grapes, must, and wine using Near-Infrared spectroscopy and machine learning algorithms. OenoOne, 54(4), pp.1105-1119.. doi.org/10.20870/oeno-one.2020.54.4.4501
    5. Vasiliki S., Gonzalez Viejo C., Szeto C., Wilkinson K.L., Torrico D.D., Pang A., De Bei R., and Fuentes S.. 2020. Classification of smoke contaminated Cabernet Sauvignon berries and leaves based on chemical fingerprinting and machine learning algorithms. Sensors 20(18): 5099. doi.org/10.3390/s20185099
    6. Jingyun O., De Bei R., Fuentes S., and Collins C.. 2020. UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications. OENO One 54(4): 1093-1103. doi.org/10.20870/oeno-one.2020.54.4.3647
    7. Fuentes, S., Summerson, V., Gonzalez Viejo, C., Tongson, E., Lipovetzky, N., Wilkinson, K.L., Szeto, C. and Unnithan, R.R., 2020. Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach. Sensors, 20(18), p.5108. doi.org/10.3390/s20185108 Recognition as Outstanding Publication and Editors Choice Article.
    8. Biju S., Fuentes S., Gonzalez Viejo C., Torrico D.D., Inayat S., and Gupta D. 2020. Silicon supplementation improves the nutritional and sensory characteristics of lentil seeds obtained from drought-stressed plants. Journal of the Science of Food and Agriculture. 01(4), 1454-1466. doi.org/10.1002/jsfa.10759
    9. Gonzalez Viejo, C., and Fuentes, S. (2020). A Digital Approach to Model Quality and Sensory Traits of Beers Fermented under Sonication Based on Chemical Fingerprinting. Fermentation, 6(3), 73. doi.org/10.3390/fermentation6030073
    10. Fuentes, S., Wong, Y. Y., and Gonzalez Viejo, C. 2020. Non-invasive Biometrics and Machine Learning Modelling to Obtain Sensory and Emotional Responses from Panelists during Entomophagy. Foods, 9(7), 903. doi.org/10.3390/foods9070903
    11. Fuentes, S., Torrico, D. D., Tongson, E., and Gonzalez Viejo, C. 2020. Machine learning modelling of wine sensory profiles and colour of vertical vintages of Pinot Noir based on chemical fingerprinting, weather, and management data. Sensors, 20(13), 3618. doi.org/10.3390/s20133618
    12. Carrasco-Benavides, Marcos, Javiera Antunez-Quilobrán, Antonella Baffico-Hernández, Carlos Ávila-Sánchez, Samuel Ortega-Farías, Sergio Espinoza, John Gajardo, Marco Mora, and Sigfredo Fuentes. 2020. "Performance Assessment of Thermal Infrared Cameras of Different Resolutions to Estimate Tree Water Status from Two Cherry Cultivars: An Alternative to Midday Stem Water Potential and Stomatal Conductance." Sensors 20(12): 3596. doi.org/10.3390/s20123596
    13. Gonzalez Viejo C., Villarreal-Lara R., Torrico D.D., Rodríguez-Velazco Y, Escobedo-Avellaneda Z, Ramos-Parra P.A., Mandal R., Singh A.P., Hernández-Brenes C, and Fuentes S.. 2020. Beer and Consumer Response Using Biometrics: Associations Assessment of Beer Compounds and Elicited Emotions." Foods 9(6) p. 821. doi.org/10.3390/foods9060821
    14. Fuentes S., Tongson E., Chen J., Gonzalez-Viejo C. 2020. A digital approach to evaluate the effect of berry cell death on quality traits and sensory profile of Pinot Noir wines using near infrared spectroscopy. Beverages 6(2), 39. doi.org/10.3390/beverages6020039 Recognition as Outstanding Publication and Editors Choice Article.
    15. Fuentes S., Gonzalez Viejo C., Cullen B., Tongson E., Chauan S., Dunshea RD. 2020. Artificial intelligence applied to a robotic dairy farm to model milk productivity and quality based on cow data and daily environmental parameters. Sensors 20(10), 2975. doi.org/10.3390/s20102975
    16. Gonzalez Viejo, C., and Fuentes, S. 2020. Beer Aroma and Quality Traits Assessment Using Artificial Intelligence. Fermentation, 6(2), 56. doi.org/10.3390/fermentation6020056
    17. Collins, C., Wang, X., Lesefko, S., De Bei, R., & Fuentes, S. 2020. Effects of canopy management practices on grapevine bud fruitfulness. OENO One, 54(2), 313-325. doi.org/10.20870/oeno-one.2020.54.2.3016
    18. Gonzalez Viejo, C., Caboche, C. H., Kerr, E. D., Pegg, C. L., Schulz, B. L., Howell, K., & Fuentes, S. 2020. Development of a Rapid Method to Assess Beer Foamability Based on Relative Protein Content Using RoboBEER and Machine Learning Modelling. Beverages, 6(2), 28. doi.org/10.3390/beverages6020028 Recognition as Outstanding Publication and Editors Choice Article.
    19. De Bei, R., Papagiannis, L., Fuentes, S., Gilliham, M., Tyerman, S., Collins, C., & Wang, X. 2020. Shoot thinning of Semillon in a hot climate did not improve yield and berry and wine quality. OENO One, 54(3), 469-484. doi.org/10.20870/oeno-one.2020.54.3.2984
    20. Torrico, D. D., Tam, J., Fuentes, S., Viejo, C. G., & Dunshea, F. R. 2020. Consumer rejection threshold, acceptability rates, physicochemical properties, and shelf‐life of strawberry‐flavored yogurts with reductions of sugar. Journal of the Science of Food and Agriculture. 100(7), 3024-3035. doi.org/10.1002/jsfa.10333
    21. Torrico, D. D., Han, Y., Sharma, C., Fuentes, S., Gonzalez Viejo, C., & Dunshea, F. R. 2020. Effects of context and virtual reality environments on the wine tasting experience, acceptability, and emotional responses of consumers. Foods, 9(2), 191. doi.org/10.3390/foods9020191
    22. Jorquera-Chavez, M., Fuentes, S., Dunshea, F.R., Warner, R.D., Poblete, T., Morrison, R.S. and Jongman, E.C., 2020. Remotely sensed imagery for early detection of respiratory disease in pigs: a pilot study. Animals, 10(3), p.451. doi.org/10.3390/ani10030451
    23. Gonzalez Viejo C., Fuentes S., Godbole A., Widdicombe B., Unnithan R.R. 2020. Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. Sensors and Actuators B Chemical. 308, p.127688 doi.org/10.1016/j.snb.2020.127688
    24. Fuentes, S., Tongson, E., Torrico, D.D. and Gonzalez Viejo, C., 2019. Modeling pinot noir aroma profiles based on weather and water management information using machine learning algorithms: A vertical vintage analysis using artificial intelligence. Foods, 9(1), p.33. doi.org/10.3390/foods9010033
    25. Jorquera-Chavez, M., Fuentes, S., Dunshea, F.R., Warner, R.D., Poblete, T. and Jongman, E.C., 2019. Modelling and validation of computer vision techniques to assess heart rate, eye temperature, ear-base temperature and respiration rate in cattle. Animals, 9(12), p.1089. doi.org/10.3390/ani9121089
    26. W. Cameron, P.R. Petrie, E.W.R. Barlow, C.J. Patrick, K. Howell, S. Fuentes. 2020. Advancement of grape maturity: comparison between contrasting cultivars and regions. Australian Journal of Grape and Wine Research, 26(1), pp.53-67 doi.org /10.1111/ajgw.12414

    1. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. Bubbles, Foam Formation, Stability and Consumer Perception of Carbonated Drinks: A Review of Current, New and Emerging Technologies for Rapid Assessment and Control. Foods. 8(12), p. 596 doi.org/10.3390/foods8120596
    2. De Bei, R., Fuentes, S., & Collins, C. 2019. Vineyard variability: can we assess it using smart technologies? Original language of the article: English. IVES Technical Reviews, vine and wine. doi.org/10.20870/IVES-TR.2019.2544
    3. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. Emerging technologies based on artificial intelligence to assess quality and consumer preference of beverages. Beverages. 5(4), p. 62 doi.org/10.3390/beverages5040062
    4. Gunaratne T.M., Gonzalez Viejo C., Gunaratne N.M., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modelling. Foods. 8(10), p. 426 doi.org/10.3390/foods8100426
    5. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S.. 2019. The Effect of Sonication on Bubble Size and Sensory Perception of Carbonated Water to Improve Quality and Consumer Acceptability. Beverages. 5(3), p.58 doi.org/10.3390/beverages5030058
    6. Condé, B., Robinson, A., Bodet, A., Monteau, A.C., Fuentes, S., Scollary, G., Smith, T. and Howell, K.S., 2019. Using synchronous fluorescence to investigate chemical interactions influencing foam characteristics in sparkling wines. Beverages, 5(3), p.54. doi.org/10.3390/beverages5030054
    7. Fuentes S., Tongson E.J., De Bei R., Gonzalez Viejo C., Ristic R., Tyerman S.D., Wilkinson K.L. 2019. Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modelling Approach. Sensors. 19(15), p. 3335 doi.org/10.3390/s19153335
    8. Gunaratne N.M., Fuentes S., Gunaratne T.M., Torrico D.D., Ashman H., Francis C., Gonzalez Viejo C., Dunshea F.R. 2019. Consumers acceptability, eye fixations, and physiological responses: A study using eye tracking devices on novel and familiar chocolate packaging designs. Foods, 8(7), p.253 doi.org/10.3390/foods8070253
    9. Torrico D.D., Tam J., Fuentes S., Gonzalez Viejo C., Dunshea F.R. 2019. D-tagatose as sucrose substitute on the physico-chemical properties and acceptability of strawberry-flavored yogurt. Foods. 8(7)., doi.org/10.3390/foods8070256
    10. Fuentes S., Chacon G., Torrico D.D., Zarate A., Gonzalez Viejo C. 2019. Spatial Variability of Aroma Profiles of Cocoa Trees Obtained Through Computer Vision and Machine Learning Modelling: A Cover Photography and High Spatial Remote Sensing Application. Sensors. 19(14)., doi.org/10.3390/s19143054
    11. Gunaratne T.M., Fuentes S., Gunaratne N.M, Torrico D.D., Gonzalez Viejo C., Dunshea F.R. 2019. Physiological Responses to Basic Tastes for Sensory Evaluation of Chocolate Using Biometric Techniques. Foods. 8(7):243., doi.org/10.3390/foods8070243
    12. Gunaratne N.M., Gonzalez Viejo C., Gunaratne T.M., Torrico D.D., Ashman H., Dunshea F.R., Fuentes S.. 2019. Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses. J, 2(2), pp.206-225. doi.org/10.3390/j2020015
    13. De Bei R., Wang X., Papagiannis L., Cocco M., O’Brien P., Zito M., Ouyang J. Fuentes S., Gilliham M., Tyerman S., Collins C. 2019. Postveraison leaf removal does not consistently delay ripening in Sémillon and Shiraz in a hot Australian climate. American Journal of Enology and Viticulture 70(4): 398-410. doi.org/10.5344/ajev.2019.18103
    14. Jorquera-Chavez M., Fuentes S., Dunshea F.R, Jongman E., Warner R.D.. 2019. Computer vision and remote sensing to assess physiological responses of cattle to pre-slaughter stress, and its impact on beef quality: A review. Meat Science. 156: 11-22. doi.org/10.1016/j.meatsci.2019.05.007
    15. Gunaratne N.M., Fuentes S., Gunaratne T.M., Torrico D.D., Francis C., Ashman H., Gonzalez Viejo C., Dunshea F.R. 2019. Effects of packaging design on sensory liking and willingness to purchase: A study using novel chocolate packaging. Heliyon, 5(6) doi.org/10.1016/j.heliyon.2019.e01696
    16. Gonzalez Viejo C., Fuentes S., Torrico D.D., Godbole A., Dunshea F.R. 2019. Chemical characterization of aromas in beer and their effect on consumers liking. Food Chemistry. 293: 479-485. doi.org/10.1016/j.foodchem.2019.04.114
    17. Gonzalez Viejo C., Torrico D.D., Dunshea F.R., Fuentes S. 2019. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: A comparative model approach to achieve an Artificial Intelligence system. Beverages. 5(2): 33. doi.org/10.3390/beverages5020033
    18. Wang X., De Bei R., Fuentes S., Collins C. 2019. Influence of Canopy Management Practices on Reproductive Performance of Semillon and Shiraz Grapevines in a Hot Climate. American Journal of Enology and Viticulture. 70(4): 360-372. doi.org/10.5344/ajev.2019.19007
    19. Tongson, E.J., Fuentes, S., Carrasco-Benavides, M. and Mora, M. (2019). Canopy architecture assessment of cherry trees by cover photography based on variable light extinction coefficient modelled using artificial neural networks. Acta Hortic. 1235, 183-188 doi.org/10.17660/ActaHortic.2019.1235.24
    20. Jinru X, Fuentes S., Poblete-Echeverría C., Gonzalez Viejo C., Tongson E., Su B. 2019. Automated Chinese medicinal plants classification based on machine learning using leaf morpho-colorimetry, fractal dimension and visible / near infrared spectroscopy. International Journal of Agricultural and Biological Engineering. 12(2), 123-131. doi.org/10.25165/j.ijabe.20191202.4637

    1. R. De Bei, S. Fuentes, M.G. Wirthensohn, D. Cozzolino, S.D. Tyerman. 2018. Feasibility study on the use of Near Infrared spectroscopy to measure water status of almond trees. Acta Horticulturae 10/2018 doi.org/10.17660/ActaHortic.2018.1219.14
    2. Gunaratne T.M., Gonzalez Viejo C., Fuentes S., Torrico D.D., Gunaratne N.M., Ashman H., Dunshea F.R. 2018. Development of emotion lexicons to describe chocolate using the Check-All-That-Apply (CATA) methodology across Asian and Western groups. Food Research International, 115 (2019): 526-534 doi.org/10.1016/j.foodres.2018.10.001
    3. Jinru X., Fan Y., Su B., Fuentes S. 2018. Assessment of Canopy Vigor Information from Kiwifruit Plants Based on a Digital Surface Model from Unmanned Aerial Vehicle Imagery. International Journal of Agricultural and Biological Engineering, pp.165-171 doi.org/10.25165/j.ijabe.20191201.4634
    4. Torrico D.D., Nguyen P-T., Liu T., Mena B., Gonzalez Viejo C., Fuentes S., Dunshea F.R. 2018. Sensory acceptability, quality and purchase intent of potato chips with reduced salt (NaCl) concentrations. LWT- Food Science and Technology; 102. pp. 347-355, doi.org/10.1016/j.lwt.2018.12.050
    5. Torrico D.D., Fuentes S., Gonzalez Viejo C., Ashman H., Dunshea F.R. 2018. Cross-cultural effects of food product familiarity on sensory acceptability and non-invasive physiological responses of consumers. Food Research International, 115, pp.439-450 doi.org/10.1016/j.foodres.2018.10.054
    6. Fuentes S, Gonzalez Viejo C, Torrico D, Dunshea F. 2018. Development of a Biosensory Computer Application to Assess Physiological and Emotional Responses from Sensory Panelists. Sensors.18(9) p. 2958. doi.org/10.3390/s18092958
    7. Gonzalez Viejo C, Fuentes S, Torrico D, Lee M, Hu Y, Chakraborty S, Dunshea F. 2018. The Effect of Soundwaves on Foamability Properties and Sensory of Beers with a Machine Learning Modelling Approach. Beverages. 4(3), p.53. doi.org/10.3390/beverages4030053
    8. Gonzalez Viejo C., Fuentes S., Howell K., Torrico D.D., Dunshea F.R. 2018. Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications. Food Control, 92, pp.72-79. doi.org/10.1016/j.foodcont.2018.04.037
    9. Gonzalez Viejo, C., Fuentes, S., Torrico, D.D. and Dunshea, F.R., 2018. Non-contact heart rate and blood pressure estimations from video analysis and machine learning modelling applied to food sensory responses: A case study for chocolate. Sensors, 18(6), p.1802.
    10. Zuniga M., Ortega-Farias S., Fuentes S., Riveros-Burgos C., Poblete-Echeverria C. 2018. Effects of three irrigation strategies on gas exchange relationships, plant water status, yield components and water productivity on grafted Carmenere grapevines. Frontiers in Agriculture. 9: 992. doi.org/10.3389/fpls.2018.00992
    11. Fuentes S., Hernández-Montes E., Escalona JM., Bota J. Gonzalez Viejo C., Poblete-Echeverría C., Tongson E., Medrano H. 2018. Automated grapevine cultivar classification and water stress assessment based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy. Computers and Electronics in Agriculture. 151: 311-318. doi.org/10.1016/j.compag.2018.06.035
    12. Gonzalez Viejo C., Fuentes S., Torrico D.D., Howell K., Dunshea F.R. 2018. Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers. Journal of Food Science, 83(5), pp.1381-1388 doi.org/10.1111/1750-3841.14114
    13. Biju S., Fuentes S., Gupta D. 2018. The use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes. Plant Physiology and Biochemistry. 127, pp. 11-24 doi.org/10.1016/j.plaphy.2018.03.005
    14. Gonzalez Viejo C., Fuentes S., Howell S., Torrico D.D., Dunshea F.R. 2018. Integration of non-invasive biometrics with sensory analysis techniques to assess acceptability of beer by consumers. 2018. Physiology & Behaviour, 200, pp. 139-147 doi.org/10.1016/j.physbeh.2018.02.051
    15. Romero M., Luo Y., Su B., Fuentes S. 2018. Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management. Computers and Electronics in Agriculture. 147., doi.org/10.1016/j.compag.2018.02.013
    16. Torrico D.D., Fuentes S., Gonzalez Viejo C., Ashman H., Gurr P.A., Dunshea F.R. 2018. Analysis of thermochromic label elements and colour transitions using sensory acceptability and eye tracking techniques. LWT- Food Science and Technology. 89, 475-481. doi.org/10.1016/j.lwt.2017.10.048

    1. Torrico D.D., Fuentes S., Gonzalez Viejo C., Ashman H., Gunaratne N.M., Gunaratne T.M., Dunshea F.R. 2017. Images and chocolate stimuli affect physiological and affective responses of consumers: A cross-cultural study. Food Quality and Preference doi.org/10.1016/j.foodqual.2017.11.010
    2. Condé B., Bouchard E., Culbert J.A., Wilkinson K.L., Fuentes S., Howell K. 2017. Soluble Protein and Amino Acid Content Affects the Foam Quality of Sparkling Wine. Journal of Agricultural and Food Chemistry 09/2017; 65(41)., doi.org/10.1021/acs.jafc.7b02675
    3. Biju S., Fuentes S., Gupta D. 2017. Silicon improves seed germination and alleviates drought stress in lentil crops by regulating osmolytes, hydrolytic enzymes and antioxidant defense system. Plant Physiology and Biochemistry 09/2017; 119., doi.org/10.1016/j.plaphy.2017.09.001
    4. Park S., Ryu D., Fuentes S., Chung H., Hernández-Montes E., O’Connell M. 2017. Adaptive Estimation of Crop Water Stress in Nectarine and Peach Orchards Using High-Resolution Imagery from an Unmanned Aerial Vehicle (UAV). Remote Sensing; 9(828)., doi.org/10.3390/rs9080828
    5. Zhang P., Wu X., Needs S., Liu D., Fuentes S., Howell K. 2017. The influence of apical and basal defoliation on the canopy structure and biochemical composition of Vitis vinifera cv. Shiraz grapes and wine. Frontiers in Chemistry; doi.org/10.3389/fchem.2017.00048
    6. Gonzalez Viejo C., Fuentes S., Torrico D.D., Howell K., Dunshea F.R. 2017. Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and artificial neural networks modelling techniques. Journal of the Science of Food and Agriculture, 98(2), pp.618-627; doi.org/10.1002/jsfa.8506.
    7. De Bei, R., Fuentes, S., Sullivan, W., Edwards, E., Tyerman, S., & Cozzolino, D. 2017. Rapid measurement of total non-structural carbohydrate concentration in grapevine trunk and leaf tissues using near infrared spectroscopy. Computers and Electronics in Agriculture, 136, 176-183. doi.org/10.1016/j.compag.2017.03.007
    8. Condé, B. C., Fuentes, S., Caron, M., Xiao, D., Collmann, R., & Howell, K. S. 2017. Development of a robotic and computer vision method to assess foam quality in sparkling wines. Food Control, 71, 383-392. doi.org/10.1016/j.foodcont.2016.07.020

2012-2016

    1. Baofeng, S., Jinru, X., Chunyu, X., Yulin, F., Yuyang, S., & Fuentes, S. 2016. Digital surface model applied to unmanned aerial vehicle based photogrammetry to assess potential biotic or abiotic effects on grapevine canopies. International Journal of Agricultural and Biological Engineering, 9(6), 119. doi.org/10.3965/j.ijabe.20160906.2908
    2. Ristic, R., Fudge, A. L., Pinchbeck, K. A., De Bei, R., Fuentes, S., Hayasaka, Y., Wilkinson, K. L. 2016. Impact of grapevine exposure to smoke on vine physiology and the composition and sensory properties of wine. Theoretical and Experimental Plant Physiology, 28(1), 67-83. doi.org/10.1007/s40626-016-0054-x
    3. Gonzalez Viejo, C., Fuentes, S., Li, G., Collmann, R., Condé, B., & Torrico, D. 2016. Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER. Food Research International, 89, 504-513. doi.org/10.1016/j.foodres.2016.08.045
    4. Zhang, P., Fuentes S., Wang Y., Deng R, Krstic M, Herderich M., Barlow E.W., and Howell K. 2016. Distribution of rotundone and possible translocation of related compounds amongst grapevine tissues in Vitis vinifera L. cv. Shiraz. Frontiers in plant science 7: 859. doi.org/10.3389/fpls.2016.00859
    5. Mora, M., Avila, F., Carrasco-Benavides, M., Maldonado, G., Olguín-Cáceres, J., & Fuentes, S. 2016. Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies. Computers and Electronics in Agriculture, 123, 195-202. doi.org/10.1016/j.compag.2016.02.011
    6. De Bei R., Fuentes S., Gilliham M., Tyerman S., Edwards E., Bianchini N., Smith J., and Collins C. 2016. VitiCanopy: A free computer App to estimate canopy vigor and porosity for grapevine. Sensors 16, no. 4: 585. doi.org/10.3390/s16040585
    7. Carrasco-Benavides, M., Mora, M., Maldonado, G., Olguín-Cáceres, J., von Bennewitz, E., Ortega-Farías, S., John Gajardo and Fuentes, S. 2016. Assessment of an automated digital method to estimate leaf area index (LAI) in cherry trees. New Zealand Journal of Crop and Horticultural Science, 44(4), 247-261. doi.org/10.1080/01140671.2016.1207670
    8. Zhang, P., Fuentes, S., Siebert, T., Krstic, M., Herderich, M., Barlow, E. W. R., & Howell, K. 2016. Terpene evolution during the development of Vitis vinifera L. cv. Shiraz grapes. Food Chemistry, 204, 463-474. doi.org/10.1016/j.foodchem.2016.02.125

    1. Conde, B.,  Peixoto, A. B., Howell, K., Xiao, Di, Fuentes, S. 2015. Assessment by image analysis of foamability and effervescence of sparkling wines during the prise de mousse and ageing process. Rev. Bras. Vitic. & Oenol. 7, 92-98.
    2. Zhang, P., Howell, K., Krstic, M., Herderich, M., Barlow, E. W. R., & Fuentes, S. 2015. Environmental factors and seasonality affect the concentration of rotundone in Vitis vinifera L. cv. Shiraz wine. PloS one, 10(7), e0133137. doi.org/10.1371/journal.pone.0133137
    3. Park, S., Nolan, A., Ryu, D., Fuentes, S., Hernandez, E., Chung, H., & O’Connell, M. 2015. Estimation of crop water stress in a nectarine orchard using high-resolution imagery from unmanned aerial vehicle (UAV). Paper presented at the MODSIM2015, 21st International Congress on Modelling and Simulation, vol 29
    4. Nolan, A.P., Park, S., O'Connell, M., Fuentes, S., Ryu, D. and Chung, H., 2015. Automated detection and segmentation of vine rows using high resolution UAS imagery in a commercial vineyard. In International Congress on Modelling and Simulation 2015: Partnering with industry and the community for innovation and impact through modelling (pp. 1406-1412). Modelling and Simulation Society of Australia and New Zealand (MSSANZ).
    5. Zhang, P., Barlow, S., Krstic, M., Herderich, M., Fuentes, S., & Howell, K. 2015. Within-vineyard, within-vine, and within-bunch variability of the rotundone concentration in berries of Vitis vinifera L. cv. Shiraz. Journal of Agricultural and Food Chemistry, 63(17), 4276-4283. doi.org/10.1021/acs.jafc.5b00590
    6. Poblete-Echeverría, C., Fuentes, S., Ortega-Farias, S., Gonzalez-Talice, J., & Yuri, J. A. 2015. Digital cover photography for estimating leaf area index (LAI) in apple trees using a variable light extinction coefficient. Sensors, 15(2), 2860-2872. doi.org/10.3390/s150202860
    7. Medrano, H., Tomás, M., Martorell, S., Escalona, J.-M., Pou, A., Fuentes, S., Bota, J. 2015. Improving water use efficiency of vineyards in semi-arid regions. A review. Agronomy for Sustainable Development, 35(2), 499-517. doi.org/10.1007/s13593-014-0280-z

    1. Lima, B., Caron, M., Needs, S., Howell, K., and Fuentes S. 2014. The use of a portable robotic sparkling wine pourer and image analysis to assess wine quality in a fast and accurate manner. Paper presented at the XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): IV 1115. doi.org/10.17660/ActaHortic.2016.1115.11
    2. López-Olivari, R., Fuentes, S., & Ortega-Farías, S. 2014. Seasonal variation of night-time sap flow of a young olive orchard: the unconsidered process for evapotranspiration estimations. Paper presented at the XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): 1112. doi.org/10.17660/ActaHortic.2016.1112.11
    3. Poblete-Echeverría, C., Sepulveda-Reyes, D., Ortega-Farias, S., Zuñiga, M., & Fuentes, S. 2014. Plant water stress detection based on aerial and terrestrial infrared thermography: a study case from vineyard and olive orchard. Paper presented at the XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): 1112. doi.org/10.17660/ActaHortic.2016.1112.20
    4. Fuentes, S., De Bei, R., Collins, M., Escalona, J., Medrano, H., & Tyerman, S. 2014. Night-time responses to water supply in grapevines (Vitis vinifera L.) under deficit irrigation and partial root-zone drying. Agricultural Water Management, 138, 1-9. doi.org/10.1016/j.agwat.2014.02.015
    5. Fuentes, S., Poblete‐Echeverría, C., Ortega‐Farias, S., Tyerman, S., & De Bei, R. 2014. Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methods. Australian Journal of Grape and Wine Research, 20(3), 465-473. doi.org/10.1111/ajgw.12098
    6. Nguyen, T., Fuentes, S., & Marschner, P. 2014. Growth and Water Use Efficiency of Capsicum annuum in a Silt Loam Soil Treated Three Years Previously With a Single Compost Application and Repeatedly Dried. International Journal of Vegetable Science, 20(3), 187-196. doi.org/10.1080/19315260.2013.764508
    7. Poblete-Echeverría, C., Ortega-Farías, S., Lobos, G., Romero, S., Ahumada, L., Escobar, A., & Fuentes, S. 2014. Non-invasive method to monitor plant water potential of an olive orchard using visible and near infrared spectroscopy analysis. Acta Horticulturae, 1057, 363-368. doi.org/10.17660/ActaHortic.2014.1057.43

    1. Nguyen, T.-T., Fuentes, S., & Marschner, P. 2013. Effect of incorporated or mulched compost on leaf nutrient concentrations and performance of Vitis vinifera cv. Merlot. Journal of Soil Science and Plant Nutrition, 13(2), 485-497. doi.org/10.4067/S0718-95162013005000038
    2. Bonada, M., Sadras, V., Moran, M., & Fuentes, S. 2013. Elevated temperature and water stress accelerate mesocarp cell death and shrivelling, and decouple sensory traits in Shiraz berries. Irrigation Science, 31(6), 1317-1331. doi.org/10.1007/s00271-013-0407-z
    3. Fuentes, S., Mahadevan, M., Bonada, M., Skewes, M., & Cox, J. 2013. Night-time sap flow is parabolically linked to midday water potential for field-grown almond trees. Irrigation Science, 31(6), 1265-1276. doi.org/10.1007/s00271-013-0403-3
    4. Bonada, M., Sadras, V. O., & Fuentes, S. 2013. Effect of elevated temperature on the onset and rate of mesocarp cell death in berries of Shiraz and Chardonnay and its relationship with berry shrivel. Australian Journal of Grape and Wine Research, 19(1), 87-94 doi.org/10.1111/ajgw.12010
    5. Escalona, J. M., Fuentes, S., Tomás, M., Martorell, S., Flexas, J., & Medrano, H. 2013. Responses of leaf night transpiration to drought stress in Vitis vinifera L. Agricultural Water Management, 118, 50-58. doi.org/10.1016/j.agwat.2012.11.018
    6. Conn, S.J., Hocking, B., Dayod, M., Xu, B., Athman, A., Henderson, S., Aukett, L., Conn, V., Shearer, M.K., Fuentes, S. and Tyerman, S.D., 2013. Protocol: optimising hydroponic growth systems for nutritional and physiological analysis of Arabidopsis thaliana and other plants. Plant Methods, 9(1), pp.1-11. doi.org/10.1186/1746-4811-9-4

    1. Poblete-Echeverría, C., Ortega-Farías, S., Zuñiga, M., Lobos, G., Romero, S., Estrada, F., & Fuentes, S. 2012. Use of infrared thermography on canopies as indicator of water stress in 'Arbequina' olive orchards. Paper presented at the VII International Symposium on Olive Growing 1057. doi.org/10.17660/ActaHortic.2014.1057.49
    2. Nguyen, T., Fuentes, S., & Marschner, P. 2012. Effects of compost on water availability and gas exchange in tomato during drought and recovery. Plant Soil and Environment, 58(11), 495-502. doi.org/10.17221/403/2012-PSE
    3. Fuentes, S., De Bei, R., Pech, J., & Tyerman, S. (2012). Computational water stress indices obtained from thermal image analysis of grapevine canopies. Irrigation Science, 30(6), 523-536. doi.org/10.1007/s00271-012-0375-8
    4. Poblete-Echeverría, C., Ortega-Farias, S., Zuñiga, M., & Fuentes, S. 2012. Evaluation of compensated heat-pulse velocity method to determine vine transpiration using combined measurements of eddy covariance system and microlysimeters. Agricultural Water Management, 109, 11-19. doi.org/10.1016/j.agwat.2012.01.019