Trabajo presentado en el ClimWine (International Symposium on Sustainable grape and wine production in the context of climate change), celebrado en Burdeos del 10 al 13 de abril de 2016.Peer Reviewe

Diago, Maria P.

Tardáguila, Javier

Digital.CSIC

New plant phenotyping technologies in a changing climate  Javier Tardaguila and María P. Diago     Understanding climate variability and climate change Schär et al., 2004  Nature Plant  phenotyping New vine phenotying technologies  Thermal imaging Spectroscopy Machine vision Non-invasive and hand-held sensors  Proximal and remote sensors in viticulture Thermal imaging Water stress and climate change Stress	  No	  stress	  Thermal imaging to assess vineyard water status Hand-held thermal imaging sensor  ( )( )wetcanopycanopydryG TTTTI−−= Mid-d y  Not exposed side of the VSP Thermal imaging to assess vineyard water status Pou et al., 2014  AWM Thermal image obtain by UAV of the vineyard Aerial image temperature (UAV)  and  gs Baluja et al, 2012  IS Mapping  of  vineyard water status Baluja et al, 2012 IS Spectroscopy •  Spectral region: 1600 – 2400 nm •  Spectral  resolution:  8 nm  •  Analyze mode: Reflectance Handheld NIRS MEMS equipment (MicroPHAZIR™  RX Analyzer) •  Sample presentation: Adaxial and Abaxial leaf surfaces  •  Five spectra for each leaf surface Hand-held NIR measurements in the vineyard 17	   Hand held NIR spectroscopy for vine water status assessment   Gutierrez et al., 2016 Sensors Machine vision Machine vision in viticulture Diago et al. (2014a) J. Sci. Food Agric. 94, 1981-1987 Precision = 92.9% correctly detected   R2 =0.81 R2 =0.87 R2 =0.83 R2 =0.81 Graciano Carignan Tempranillo All varieties Assessment of flower number under field conditions by machine vision Diago et al., 2015 JSFA Berry number and cluster weight  by image analysis under lab conditions  Variety Berry Number per Cluster Prediction, R2  Cluster Weight Prediction,  R2  Bobal  0.95 0.91 Cabernet Sauvignon  0.76 0.75 Carignan  0.95 0.65 Grenache  0.79 0.85 Merlot  0.69 0.69 Mourvedre  0.94 0.91 Tempranillo  0.91 0.97 Diago et al., 2015 JSFA Berry number and cluster weight  by image analysis under lab conditions Cluster compactness assessment by image analysis Cubero et al., 2015 AJGWR Cluster compactness assessment by image analysis Cubero et al., 2015 AJGWR Predictive capability: 85% Original image Extraction of berry candidates Final result after false positive filtering Berry number per cluster by image analysis under field conditions Canopy assessment on-the-go by machine vision Pruning weight assessment on-the-go 29	  VineRobot, a new multi-sensor platform  for vineyard monitoring  Visión por computador o Visión artificial      VineRobot Conclusions q  Plant phenotyping is even more crucial in scenarios of high environment variability occurring under climate change q  New, non-invasive sensing technologies, including computer vision, thermography and spectroscopy can be used in viticulture q  Several non-invasive sensors can be embedded and mounted on a vehicle or in a robot for field high-throughput plant phenotyping, enabling the assessment of multiple viticultural features simultaneously televi.s.unirioja.es	   32 

New plant phenotyping technologies in a changing climate

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