If you have done interesting research in Digital Agriculture or Smart Farms, please consider publishing in this SI – Free of charge!
If you have done interesting research in Digital Agriculture or Smart Farms, please consider publishing in this SI – Free of charge!
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
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
Link to the full paper: CLICK HERE
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…
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.
Read the full article by clicking HERE Abstract Labels concepts, information, logo, figures, and colors for beverages are critical for consumer perception, preference, and purchase intention. This is especially relevant for new beverage products. During social isolation, many sensory laboratories were unable to provide services, making virtual sensory sessions relevant to study different label concepts and design preferences among consumers. This study proposed a novel virtual sensory system to analyze coffee labels using videoconference, self-reported and biometric analysis software from video-recordings to obtain sensory and emotional responses from 69 participants (Power analysis: 1 – β > 0.99) using six different label…
Digital Agriculture (DA) deals with the implementation and integration of digital data, sensors, and tools on agricultural, food, and wine applications from the paddock/vineyard to consumers. These technologies can range from big data, sensor technology, sensor networks, remote sensing, robotics, unmanned aerial vehicles (UAV). Data processing is performed using new and emerging technologies, such as computer vision, machine learning, and artificial intelligence, among others. The latest advances made by the DAFW group for crop monitoring/decision making, assessment of the quality of produces, sensory analysis for consumer perception and animal stress, and welfare assessment. Visit Website by clicking HERE
To read the full paper click HERE Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: i) low-density smoke exposure (LS), ii) high-density smoke exposure (HS) and iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: iv) control (C; no smoke treatment) and v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and 7 neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1).…
A recent paper published describes the development of low-cost E-nose: Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. Claudia Gonzalez Viejo; Sigfredo Fuentes; Amruta Godbole; Bryce Widdicombe and Ranjith R Unnithan. CLICK HERE to see the paper published in Sensors and Actuators B Chemical A new paper published by the Digital Agriculture, Food and Wine research group describes the development of a low-cost E-nose based on nine gas sensors and integrated temperature and relative humidity sensors. This E-nose has been tested in a smoke contamination trial in Adelaide to detect smoke-related compounds in grapes and wines using Machine Learning and Artificial Intelligence. This E-nose can be integrated with IoT and a computer application (app) to…