New EBook from the Digital Agriculture, Food and Wine Group: “Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems”
Link to Full EBook: CLICK HERE
The Vineyard of The Future
Climate Change Research
A new paper entitled: “Assessment of the vineyard water footprint by using ancillary data and EEFlux satellite images. Examples in the Chilean Central Zone” has been published in the Journal of Science of the Total Environment.
Authors: Marcos Carrasco-Benavidesa; Samuel Ortega-Farías; Pilar M. Gil; Daniel Knopp; Luis MoralesSalinas; L. Octavio Lagos; Daniel de la Fuente; Rafael López-Olivari and Sigfredo Fuentes.
Link to paper: CLICK HERE
A.Prof. Sigfredo Fuentes has been invited to two major grapevine conferences in 2021 and 2022:
- XIth International Symposium of Grapevine Physiology and Biotechnology (November 2021, Stellenbosch, South Africa). “Artificial Intelligence Advances for Digital Viticulture and Oenology”.
- XIII International Symposium on Grapevine Breeding and Genetics (July 2022, Landau, Germany). “New and emerging digital technologies for plant phenotyping using Artificial Intelligence (AI)”.
To read the full paper click here
Advances in early insect detection have been reported using digital technologies through camera systems, sensor networks, and remote sensing coupled with machine learning (ML) modeling. However, up to date, there is no cost-effective system to monitor insect presence accurately and insectplant interactions. This paper presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning. Several artificial neural network (ANN) models were developed based on classification to detect the level of infestation and regression to predict insect numbers for both e-nose and NIR inputs, and plant physiological response based on e-nose to predict photosynthesis rate (A), transpiration (E) and stomatal conductance (gs). Results showed high accuracy for classification models ranging within 96.5–99.3% for NIR and between 94.2–99.2% using e-nose data as inputs. For regression models, high correlation coefficients were obtained for physiological parameters (gs, E and A) using e-nose data from all samples as inputs (R = 0.86) and R = 0.94 considering only control plants (no insect presence). Finally, R = 0.97 for NIR and R = 0.99 for e-nose data as inputs were obtained to predict number of insects. Performances for all models developed showed no signs of overfitting. In this paper, a field-based system using
unmanned aerial vehicles with the e-nose as payload was pro
Link to the paper: CLICK HERE
Early detection of beer faults is an important assessment in the brewing process to secure a high-quality product and consumer acceptability. This study proposed an integrated AI system for smart detection of beer faults based on the comparison of near-infrared spectroscopy (NIR) and a newly developed electronic nose (e-nose) using machine learning modelling.
Smelling Faults in beers using AI and a low-cost e-nose
Digital Agriculture, Food and Wine research group received a 2021 Grants4Ag. Bayer Crop Science Awards 2021
After receiving more than 600 proposals from almost 40 countries, the Bayer Crop Science team has selected 24 proposals to fund. From protecting plants with beneficial bacteria to detecting disease through drones and AI, these 24 scientists have outstanding innovations to help farmers protect crops. Congratulations to the 2021 Grants4Ag awardees!
Click HERE to go to the site, and HERE to know more about the project funded.
Click here for more information
Dear Colleagues,
Recent digital developments in food, beverage, and packaging analysis have been focused on using contactless sensors, such as remote sensing and biometrics, to assess the quality traits of produces, detect packaging defects during the production process, and understand perception through sensory analysis based on consumer biometrics. The implementation of these novel techniques is aiding the food, beverage, and packaging industries in the rapid, efficient, cost-effective, and reliable evaluation and assessment of products at any stage of the production chain for the early detection of potential faults within the process and consumer evaluation of the final product and packaging.
This Special Issue will bring together high-quality papers focused on these technologies and their applications in the food and beverage industries, including packaging assessments. These contactless sensors incorporate electronic noses and tongues, the use of infrared thermal cameras, UV-Vis, mid- and near-infrared spectroscopy, and computer vision analysis, among others. These technologies may be applied to assess food, beverages, and packaging quality traits such as sensory attributes, physicochemical components, nutritional values, and defects, at any stage in the production chain, which aid in the quality assessment of products.
Dr. Claudia Gonzalez Viejo
Dr. Sigfredo Fuentes
Dr. Damir Torrico
Guest Editors
https://findanexpert.unimelb.edu.au/news/6533-using-tech-to-save-wine-from-bushfire-smoke
Grapevine smoke exposure and the subsequent development of smoke taint in wine has resulted in significant financial losses for grape growers and winemakers throughout the world. Smoke taint is characterized by objectional smoky aromas such as “ashy”, “burning rubber”, and “smoked meats”, resulting in wine that is unpalatable and hence unprofitable. Unfortunately, current climate change models predict a broadening of the window in which bushfires may occur and a rise in bushfire occurrences and severity in major wine growing regions such as Australia, Mediterranean Europe, North and South America, and South Africa. As such, grapevine smoke exposure and smoke taint in wine are increasing problems for growers and winemakers worldwide. Current recommendations for growers concerned their grapevines have been exposed to smoke are to conduct pre-harvest mini-ferments for sensory assessment and send samples to a commercial laboratory to quantify levels of smoke-derived volatiles in the wine. Significant novel research is being conducted using spectroscopic techniques coupled with machine learning modeling to assess grapevine smoke contamination and taint in grapes and wine, offering growers and winemakers additional tools to monitor grapevine smoke exposure and taint rapidly and non-destructively in grapes and wine.
To read the full paper click HERE