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PreviewIssue DateTitleAuthor(s)
2010A novel analysis of grapevine berry tissue demonstrates a variety-dependent correlation between tissue vitality and berry shrivelFuentes, S.; Sullivan, W.; Tilbrook, J.; Tyerman, S.
2013Protocol: optimising hydroponic growth systems for nutritional and physiological analysis of Arabidopsis thaliana and other plantsConn, S.; Hocking, B.; Dayod, M.; Xu, B.; Athman, A.; Henderson, S.; Aukett, L.; Conn, V.; Shearer, M.; Fuentes, S.; Tyerman, S.; Gilliham, M.
2014Automated estimation of leaf area index from grapevine canopies using cover photography, video and computational analysis methodsFuentes, S.; Poblete-Echeverría, C.; Ortega-Farias, S.; Tyerman, S.; de Bei, R.
2012Computational water stress indices obtained from thermal image analysis of grapevine canopiesFuentes, S.; De Bei, R.; Pech, J.; Tyerman, S.
2011Non-destructive measurement of grapevine water potential using near infrared spectroscopyDe Bei, R.; Cozzolino, D.; Sullivan, W.; Cynkar, W.; Fuentes, S.; Dambergs, R.; Pech, J.; Tyerman, S.
2016Impact of grapevine exposure to smoke on vine physiology and the composition and sensory properties of wineRistic, R.; Fudge, A.; Pinchbeck, K.; De Bei, R.; Fuentes, S.; Hayasaka, Y.; Tyerman, S.; Wilkinson, K.
2014Night-time responses to water supply in grapevines (Vitis vinifera L.) under deficit irrigation and partial root-zone dryingFuentes, S.; De Bei, R.; Collins, M.; Escalona, J.; Medrano, H.; Tyerman, S.
2019Non-invasive tools to detect smoke contamination in grapevine canopies, berries and wine: a remote sensing and machine learning modeling approachFuentes, S.; Tongson, E.; De Bei, R.; Viejo, C.; Ristic, R.; Tyerman, S.; Wilkinson, K.