New Paper: Development of a robotic pourer constructed with ubiquitous materials, open hardware and sensors to assess beer foam quality using computer vision and pattern recognition algorithms: RoboBEER


There are currently no standardized objective measures to assess beer quality based on the most significant parameters related to the first impression from consumers, which are visual characteristics of foamability, beer color and bubble size. This study describes the development of an affordable and robust robotic beer pourer using low-cost sensors, Arduino® boards, Lego® building blocks and servo motors for prototyping. The RoboBEER is also coupled with video capture capabilities (iPhone 5S) and automatedpost hoc computer vision analysis algorithms to assess different parameters based on foamability, bubble size, alcohol content, temperature, carbon dioxide release and beer color. Results have shown that parameters obtained from different beers by only using the RoboBEER can be used for their classification according to quality and fermentation type. Results were compared to sensory analysis techniques using principal component analysis (PCA) and artificial neural networks (ANN) techniques. The PCA from RoboBEER data explained 73% of variability within the data. From sensory analysis, the PCA explained 67% of the variability and combining RoboBEER and Sensory data, the PCA explained only 59% of data variability. The ANN technique for pattern recognition allowed creating a classification model from the parameters obtained with RoboBEER, achieving 92.4% accuracy in the classification according to quality and fermentation type, which is consistent with the PCA results using data only from RoboBEER. The repeatability and objectivity of beer assessment offered by the RoboBEER could translate into the development of an important practical tool for food scientists, consumers and retail companies to determine differences within beers based on the specific parameters studied.

New Article:Development of a robotic and computer vision method to assess foam quality in sparkling wines.

Quality assessment of food products and beverages might be performed by the human senses of smell, taste, sound and touch. Likewise, sparkling wines and carbonated beverages are fundamentally assessed by sensory evaluation. Computer vision is an emerging technique that has been applied in the food industry to objectively assist quality and process control. However, publications describing the application of this novel technology to carbonated beverages are scarce, as the methodology requires tailored techniques to address the presence of carbonation and foamability. Here we present a robotic pourer (FIZZeyeRobot), which normalizes the variability of foam and bubble development during pouring into a vessel. It is coupled with video capture to assess several parameters of foam quality, including foamability (the ability of the foam to form) drainability (the ability of the foam to resist drainage) and bubble count and allometry. The foam parameters investigated were analyzed in combination to the wines scores, chemical parameters obtained from laboratory analysis and manual measurements for validation purposes. Results showed that higher quality scores from trained panelists were positively correlated with foam stability and negatively correlated with the velocity of foam dissipation and the height of the collar. Significant correlations were observed between the wine quality measurements of total protein, titratable acidity, pH and foam expansion. The percentage of the wine in the foam was found to promote the formation of smaller bubbles and to reduce foamability, while drainability was negatively correlated to foam stability and positively correlated with the duration of the collar. Finally, wines were grouped according to their foam and bubble characteristics, quality scores and chemical parameters. The technique developed in this study objectively assessed foam characteristics of sparkling wines using image analysis whilst maintaining a cost-effective, fast, repeatable and reliable robotic method. Relationships between wine composition, bubble and foam parameters obtained automatically, might assist in unraveling factors contributing to wine quality and directions for further research.

New Article:Assessment of an automated digital method to estimate leaf area index (LAI) in cherry trees

A study was carried out during two growing seasons to evaluate the performance of a digital photography method to estimate the LAI (LAID). The trial consisted in 10 ‘Bing’ and 10 ‘Sweetheart’ trees where actual LAI (LAIA) was obtained by allometric relations. Estimations of LAID were obtained by the batch processing of images of the canopy of the same trees which were obtained by a conventional digital RGB camera. Comparisons of averages LAIA and LAID resulted in a good level of agreement for ‘Sweetheart’ for the two growing seasons (mean absolute percent error: MAE% = 10.4 %). For ‘Bing’, LAID was accurate in the first growing season (MAE% = 17.7 %) but underestimated by 44% (MAE%) in the second growing season, presumably due to differences observed in the clumping index and the light extinction coefficient. Results evidenced the robustness of this simple method for determining the LAI of cherry trees.

Research from VoF will be applied to the streets of Melbourne!

Monitoring the Green Infrastructure of Cities by integrating an automated IoT robotic system on top of public transport and cloud computing.

Funding body: Melbourne Networked Society Institute.


Climate Change have resulted already in heat wave events becoming more frequent and severe in Australian cities and have put significant pressure into developing efficient systems to maintain and increase the urban green infrastructure to avoid tree dieback and eventually complete loss of urban trees. This effect can increase the “urban heat island effect” and its associated detrimental consequences for the society’s wellbeing and biodiversity of cities. Currently, there are no systematic tools to monitor the green infrastructure of cities to respond efficiently and in timely manner to demands imposed by the changing environment on urban trees.

This project proposes the integration of a monitoring tool and decision-making system based on infrared/visible robotic cameras mounted on top of buses and public service vehicles from cities, such as trams (Figs. 1 & 2 named as IR-Busmonitor). The main aim of the system is to obtain geo-referenced upward-looking imagery using internet of things technology (IoT) for data capture and transmission. Cloud computing and developed algorithms will be then used to obtain physiological information from tree canopies to create high temporal and spatial resolution information from the urban green infrastructure. The system will allow to: increase in the long term the green infrastructure of cities, offset carbon emissions by public services, carbon sequestration, recreational places for the public, ameliorate detrimental heat wave effects on population and biodiversity and decrease heating/cooling costs for the public through understanding the risk and resilience of green infrastructure in response to heat waves. The major novelty of the system proposed is that it will effectively transform public transport and service vehicles from cities into networked monitoring robots at affordable costs compared to alternatives, such as satellite imagery acquisition and analysis and the use of drones or unmanned aerial systems (UAS). The latter been highly regulated within urban environments through the Civil Aviation Safety Authority (CASA) and perceived by the public as invasive of privacy and security. This project also addresses specific activities, such as water and urban futures with emphasis in the application to novel adaptation strategies to climatic anomalies, such as heat wave events to be available to urban environments, the use of pervasive information through integrative technology and entrepreneurship by developing a novel product and service that can be easily replicated in major cities within Australia and around the world.

Expected Project Outcomes

One of the biggest challenges in the future for urban communities is to decrease detrimental effects of the Urban Heat Island effect and extreme events such as heat waves. Inadequate or inefficient management of the green infrastructure leads to tree dieback and complete loss of trees, associated to high costs for councils and community to replace them to the pre-mortality growth level. This funding will help understanding the short and long term effects of events mentioned before on the green infrastructure of the cities and provide an integrated system for city councils to acquire data, automatically analyse it and have processed information readily accessible for decision making and management. Specifically, maintaining and increasing the green infrastructure from cities offers a wide range of benefits to the communities and the environment, such as:


  • Death rates in heat-waves for people over 65 years old
  • Respiratory problems on people below 16 years old
  • Tree dieback
  • Stress of aquatic ecosystems by degreasing temperature of storm water
  • Heating and cooling costs for inhabitants in general
  • Run-off and therefore erosion
  • Individual on-site inspection of treesIncreasing:Due to the versatility and network based concept of the system proposed, the number of people that can be benefited will be according to the general population of cities in which the system is put into practice with a national impact considering major cities in Australia.