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How AI and MATLAB Are Helping Winegrowers Analyse Bushfire Smoke Contamination
Sigfredo Fuentes
sfuentes@unimelb.edu.au
Associate Professor in Digital Agriculture,
Food and Wine Sciences
https://www.researchgate.net/profile/Sigfredo_FuentesSchool of Agriculture and Food
S e n s o r y L a b o r a t o r y
FACULTY OF VETERINARY & AGRICULTURAL SCIENCES
D i g i t a l A g r i c u l t u r e L a b o r a t o r y
Photo: James Morgan
The v ineyard of the future in i t iat ivewww.vineyeardofthefuture.com
Dig
ita
l V
itic
ult
ure
Photo: Sonja Needs
Inspector Paw App
The v ineyard of the future in i t iat ivewww.vineyeardofthefuture.com
Automated Recognit ion of Catt le Features for Data Extract ion
Monitoring Catt le BiometricsRespiration Rate: IR-Non radiometric Body Temperature: InfraRed Thermography Radiometric
Heart Rate: Video Magnification Analysis
Fuentes et al 2020. Under review
Big Data andMachine Learning to achieve Artificial Intelligence to maximize productivity and quality of milk in a robotic dairy farm
A r t i f i c i a l I nte l l i g e n c e A p p l i c a t i o n to M i n i m i s e D a r k C u tt i n g B e e f ( D C B )
Inputs: Non-contact Animal BiometricsTarget: Minimise Dark Cutting Beef (DCB)
Automatic Robotic Pourer to assess foamability (RoboBEER)
• 14 Peer Reviewed Papers since 2014• Featured in Science and Forbes Magazines
How AI and MATLAB Are Helping Winegrowers Analyse Bushfire Smoke Contamination
Sigfredo Fuentes
sfuentes@unimelb.edu.au
Associate Professor in Digital Agriculture,
Food and Wine Sciences
https://www.researchgate.net/profile/Sigfredo_FuentesSchool of Agriculture and Food
S e n s o r y L a b o r a t o r y
FACULTY OF VETERINARY & AGRICULTURAL SCIENCES
D i g i t a l A g r i c u l t u r e L a b o r a t o r y
Source: NASA
Glo
ba
l W
arm
ing
Glo
ba
l W
arm
ing
Bu
shfi
re E
ve
nts
Source:Hormick, 2019
Smoke Contamination / Taint: Machine Learning modell ing
Smoke Contamination / Taint: Machine Learning modell ing
Smoke Contamination / Taint: Machine Learning model l ing
2019
Smoke detection in Canopies
Canopy Conductance
Infr
are
d In
dex
Canopy Conductance
Infr
are
d In
dex
Stomata
Stomata
Training = 97%Test = 93%Overall = 96%
Sequential order weight and bias
Smoke detection in Canopies
Training = 91%Test = 91%Overall = 93%
Sequential order weight and bias
Smoke detection in Berries
Smoke detection in Canopies and Berries
Development of an e – Nose coupled with Machine Learning
Example of outputs
Electronic board + Sensors
Gas Sensors (x9) Gas Chromatography outputs
PorterSteam Ale
Development of an e – Nose coupled with Machine Learning
Example of outputs
Electronic board + Sensors
Gas Sensors (x9) Gas Chromatography outputs
PorterSteam Ale
Development of an e – Nose coupled with Machine Learning
Example of outputs
Electronic board + Sensors
Gas Sensors (x9) Gas Chromatography outputs
PorterSteam Ale
Software Development• BioSensory Computer App
Software Development
• BioSensory Computer App
a)
b)
c)
d)
Integration of technologies:From Tree to the Palate
UNMANNED AERIAL SYSTEMS
Spatial mapping of NDVI and infrared imagery
PROXIMAL REMOTE SENSING
Plant water statusVigourFertilizer demandLeaf Area IndexFruit recognition
Using phone attachments and apps to capture visible and infrared images
Remote sensing
Harvest for sensory analysis
Liking and sensory profile to be related with field data
S e n s o r y L a b o r a t o r yFACULTY OF VETERINARY & AGRICULTURAL SCIENCES
Ground-truth for remotely sensed information
Non-destructive/ GCMS assessment
How AI and MATLAB Are Helping Winegrowers Analyse Bushfire Smoke Contamination
Sigfredo Fuentes
sfuentes@unimelb.edu.au
Associate Professor in Digital Agriculture,
Food and Wine Sciences
https://www.researchgate.net/profile/Sigfredo_FuentesSchool of Agriculture and Food
S e n s o r y L a b o r a t o r y
FACULTY OF VETERINARY & AGRICULTURAL SCIENCES
D i g i t a l A g r i c u l t u r e L a b o r a t o r y
Thank You
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