Sigfredo Fuentes

Dr. Sigfredo Fuentes is an associate professor in Digital Agriculture, Food and Wine Sciences (DAFW) at the School of Agriculture, Food and Ecosystem Sciences (SAFES) in the Faculty of Science at The University of Melbourne, Australia. In addition, Sigfredo Fuentes is the international coordinator of the Vineyard of The Future initiative (VoF), which is an international collaboration that strives to establish a fully instrumented vineyard that uses the Internet of Things (IoT) for data capture and meaningful analysis of climate change in viticulture and enology in Australia, to transfer these technologies to all agricultural sectors.

He has over 30 projects and 216 scholarly works, both active and completed. He is working on two major projects: “Deep learning modeling for hyperspectral imagery” funded by the Australian Government Department of Defence and the Centre of Excellence in Plants for Space (P4S), which has the goal to develop plants to support long-term space missions to the Moon (2030) and Mars (2040) with the NASA.

His research efforts focus on the application of technology to plant physiology and viticulture, as well as in the areas of irrigation science, irrigation scheduling, remote sensing, and climate change. His work also focuses on the development of computer programs for agricultural research and applications, the creation of new methodologies to assess plant physiology and growth using image analysis and technology, and the use of machine learning and artificial intelligence (AI) in agricultural, food, animal, and wine sciences.

Sigfredo Fuentes joined Tecnológico de Monterrey as a Distinguished Visiting Professor in Digital Agriculture and Food Sciences for the School of Engineering and Sciences.

•  Ph.D., Plant Physiology, Western Sydney University
• Agronomist, University of Talca
• B.Sc., Agricultural Sciences, University of Talca
   

• Shin, M., Gonzalez Viejo, C., Tongson, E., Wiechel, T., Taylor, P. & Fuentes, S. (2023). Early detection of Verticillium wilt of potatoes using near-infrared spectroscopy and machine learning modeling. Computers and Electronics in Agriculture, 204 doi:10.1016/j.compag.2022.107567

• Harris, N., Gonzalez Viejo, C., Barnes, C. & Fuentes, S. (2022). Non-Invasive Digital Technologies to Assess Wine Quality Traits and Provenance through the Bottle. Fermentation, 9(1), pp. 10-10. doi:10.3390/fermentation9010010

• Fuentes, S., Gonzalez Viejo, C., Tongson, E., Dunshea, F., Dac, H. & 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 doi:10.1016/j.jafr.2022.100388

• Ashfaq, W., Brodie, G., Fuentes, S. & Gupta, D. (2022). Infrared Thermal Imaging and Morpho-Physiological Indices Used for Wheat Genotypes Screening under Drought and Heat Stress. PLANTS-BASEL, 11(23), pp. 22-. doi:10.3390/plants11233269

• Dac, H. H., Gonzalez Viejo, C., Lipovetzky, N., Tongson, E., Dunshea, F. R. & Fuentes, S. (2022). Livestock Identification Using Deep Learning for Traceability. SENSORS, 22(21), pp. 19-. doi:10.3390/s22218256

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