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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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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Dr. Thomas Kurfess is chief manufacturing officer at the Georgia Institute of Technology (GT) and executive director of the Georgia Tech Manufacturing Institute (GTMI), as well as HUSCO Ramirez Distinguished Chair in Fluid Power and Motion Control at GT. He is also Regents’ Professor at the Georgia Institute of Technology and professor and BMW Chair of Manufacturing Emeritus at Clemson University. Previously, he served as assistant director for the Advanced Manufacturing Office of Science and Technology Policy in the Executive Office of the President of the United States of America
(2012-2013) and as chief manufacturing officer of the Oak Ridge National Laboratory (2019- 2021).

During the early stage of his career, he was an associate professor at Carnegie Mellon University. He has taught courses in subjects such as advanced manufacturing systems; quality control and quality assurance; vehicle testing; automotive electronics; real-time signal processing and control; precision systems and manufacturing; and robotics and advanced dynamics. 

Thomas Kurfess’ research focuses on the design and development of advanced manufacturing systems by integrating new technologies into production operations and on national and international manufacturing policy issues.

He is an elected member of the U.S. National Academy of Engineering; chair of the U.S. National Academy of Engineering (NAE) study called “Options for a National Plan for Smart Manufacturing”; member of the U.S. National Materials and Manufacturing Board; and member of the Board of Governors of the
American Society of Mechanical Engineers (ASME), among others.

Thomas Kurfess joined Tecnológico de Monterrey as a Distinguished Visiting Professor in Advanced Manufacturing for the School of Engineering and Sciences.

• Ph.D., Mechanical Engineering, Massachusetts Institute of Technology (MIT)
•  S.M., Electrical Engineering and Computer Science, MIT
• S.M., Mechanical Engineering, MIT
• S.B., Mechanical Engineering, MIT 
   

• Lu, L. Kurfess, T.R., Saldana, C.J., “Effects of Extrinsic Noise Factors on Machine Learning Based Chatter Detection in Machining,” International Journal of Smart and Sustainable Manufacturing, 2021, accepted.

• Miers, J.C, Tucker, T.M., Kurfess, T.R., Saldana, C.J., “Voxel-Based Modeling of Transient Material Removal in Machining,” International Journal of Advanced Manufacturing Technology, 2021, accepted.

• Parto Dezfouli, M., Urbina Coronado, Saldana, C.J., Kurfess, T.R., “Cyber-Physical System Implementation for Manufacturing with Analytics in the Cloud Layer,” ASME Journal of Computing and Information Science in Engineering, 2021.

• Feldhausen, T., Saleeby, K., Love, L., Kurfess, T.R., “Spinning the Digital Thread with Hybrid Manufacturing,” ASME Smart and Manufacturing Letters, Vol. 29, pp. 15-18, doi.org/10.1016/j.mfglet.2021.05.003, 2021.

• Castro‐Martin, A.P., Ahuett‐Garza, H., Guamán‐Lozada, D., Márquez‐Alderete, M.F., Urbina Coronado, P.D. 1, Orta Castañon, P.A., Kurfess, T.R., González de Castilla, E., “Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In‐Line Metrology System,” Applied Science, Vol. 11, doi.org/10.3390/app11031312, 2021

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Dr. Thomas Kurfess is chief manufacturing officer at the Georgia Institute of Technology (GT) and executive director of the Georgia Tech Manufacturing Institute (GTMI), as well as HUSCO Ramirez Distinguished Chair in Fluid Power and Motion Control at GT. He is also Regents’ Professor at the Georgia Institute of Technology and professor and BMW Chair of Manufacturing Emeritus at Clemson University. Previously, he served as assistant director for the Advanced Manufacturing Office of Science and Technology Policy in the Executive Office of the President of the United States of America
(2012-2013) and as chief manufacturing officer of the Oak Ridge National Laboratory (2019- 2021).

During the early stage of his career, he was an associate professor at Carnegie Mellon University. He has taught courses in subjects such as advanced manufacturing systems; quality control and quality assurance; vehicle testing; automotive electronics; real-time signal processing and control; precision systems and manufacturing; and robotics and advanced dynamics. 

Thomas Kurfess’ research focuses on the design and development of advanced manufacturing systems by integrating new technologies into production operations and on national and international manufacturing policy issues.

He is an elected member of the U.S. National Academy of Engineering; chair of the U.S. National Academy of Engineering (NAE) study called “Options for a National Plan for Smart Manufacturing”; member of the U.S. National Materials and Manufacturing Board; and member of the Board of Governors of the
American Society of Mechanical Engineers (ASME), among others.

Thomas Kurfess joined Tecnológico de Monterrey as a Distinguished Visiting Professor in Advanced Manufacturing for the School of Engineering and Sciences.

• Ph.D., Mechanical Engineering, Massachusetts Institute of Technology (MIT)
•  S.M., Electrical Engineering and Computer Science, MIT
• S.M., Mechanical Engineering, MIT
• S.B., Mechanical Engineering, MIT 
   

• Lu, L. Kurfess, T.R., Saldana, C.J., “Effects of Extrinsic Noise Factors on Machine Learning Based Chatter Detection in Machining,” International Journal of Smart and Sustainable Manufacturing, 2021, accepted.

• Miers, J.C, Tucker, T.M., Kurfess, T.R., Saldana, C.J., “Voxel-Based Modeling of Transient Material Removal in Machining,” International Journal of Advanced Manufacturing Technology, 2021, accepted.

• Parto Dezfouli, M., Urbina Coronado, Saldana, C.J., Kurfess, T.R., “Cyber-Physical System Implementation for Manufacturing with Analytics in the Cloud Layer,” ASME Journal of Computing and Information Science in Engineering, 2021.

• Feldhausen, T., Saleeby, K., Love, L., Kurfess, T.R., “Spinning the Digital Thread with Hybrid Manufacturing,” ASME Smart and Manufacturing Letters, Vol. 29, pp. 15-18, doi.org/10.1016/j.mfglet.2021.05.003, 2021.

• Castro‐Martin, A.P., Ahuett‐Garza, H., Guamán‐Lozada, D., Márquez‐Alderete, M.F., Urbina Coronado, P.D. 1, Orta Castañon, P.A., Kurfess, T.R., González de Castilla, E., “Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In‐Line Metrology System,” Applied Science, Vol. 11, doi.org/10.3390/app11031312, 2021

More publications