Category: Research Paper

New Interview: Digital Vineyards: building technology so farms can think for themselves

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Dr Sigfredo Fuentes is a plant physiologist and engineer at the University of Melbourne whose end game is the smart farm.

In his mind, it won’t be restricted to a single crop – maybe it’ll grow peaches and nectarines and vines as well – all of which are mapped out digitally.

And at the heart of the operation will be the drone, autonomous and equipped to collect all the data it needs to monitor the entire farm down to the last plant or tree.

And it’s fast – it can cover 2500 hectares in a day, which is about 2000 football fields.

Link to full article: CLICK HERE

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

Abstract

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.

Abstract
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.
Fig-1-Diagram-illustrating-the-procedure-used-for-quantifying-foam-parameters-by-using
Pourer

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

Abstract
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.
CherryLAI

New Article: Distribution of rotundone and possible translocation of related compounds amongst grapevine tissues in Vitis vinifera L. cv. Shiraz

ArticleinFrontiers in Plant Science · June 2016

Impact Factor: 3.95 ·
Abstract
Rotundone is an attractive wine aroma compound, especially important for cool climate Shiraz. Its presence in wine is mainly fromthe grape skin, but can also be found in non-grape tissues, such as leaves and stems. Whether rotundone is produced independently within different grapevine tissues or transported amongst non-grape tissues and grape berries remains unclear. The current study investigated the distribution of this compound in different vine tissues during development and studied the most likely mode of rotundone translocation – via phloem – using stable isotope feeding. In addition, local production of rotundone induced by herbivore feeding was assessed. Results showed that rotundone was firstly detected in the petioles and peduncles/rachises within the development of Vitis vinifera L. cv. Shiraz. Different grapevine tissues had a similar pattern of rotundone production at different grape developmental stages. In the individual vine shoots, non-grape tissues contained higher concentrations and amounts of rotundone compared to berries, which showed that non-grape tissues were the larger pool of rotundone within the plant. This study confirmed the local production of rotundone in individual tissues and ruled out the possibility of phloem translocation of rotundone between different tissues. In addition, other terpenes, including 1 monoterpenoid (geraniol) and six sesquiterpenes (clovene, α‐ylangene, β‐copaene, α‐muurolene, δ‐cadinene, and cis/trans‐calamenene) were, for the first time, detected in the EDTA-facilitated petiole phloem exudates, with their originality unconfirmed. Unlike other herbivore-induced terpenes, herbivorous activity had limited influences on the concentration of rotundone in grapevine leaves.
FULL PAPER: CLICK HERE