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The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas.

Artificial Intelligence. --- Sensor networks. --- Agriculture. --- Forests and forestry.

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The implementation of artificial intelligence (AI), together with robotics, sensors, sensor networks, Internet of Things (IoT), and machine/deep learning modeling, has reached the forefront of research activities, moving towards the goal of increasing the efficiency in a multitude of applications and purposes related to environmental sciences. The development and deployment of AI tools requires specific considerations, approaches, and methodologies for their effective and accurate applications. This Special Issue focused on the applications of AI to environmental systems related to hazard assessment in urban, agriculture, and forestry areas.

Artificial Intelligence. --- Sensor networks. --- Agriculture. --- Forests and forestry.

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In recent years, new and emerging digital technologies applied to food science have been gaining attention and increased interest from researchers and the food/beverage industries. In particular, those digital technologies that can be used throughout the food value chain are accurate, easy to implement, affordable, and user-friendly. Hence, this Special Issue (SI) is dedicated to novel technology based on sensor technology and machine/deep learning modeling strategies to implement artificial intelligence (AI) into food and beverage production and for consumer assessment. This SI published quality papers from researchers in Australia, New Zealand, the United States, Spain, and Mexico, including food and beverage products, such as grapes and wine, chocolate, honey, whiskey, avocado pulp, and a variety of other food products.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- sensory --- physicochemical measurements --- artificial neural networks --- near infra-red spectroscopy --- wine quality --- machine learning modeling --- weather --- consumer acceptance prediction --- data fusion --- emotion recognition --- facial expression recognition --- galvanic skin response --- machine learning --- neural networks --- sensory analysis --- avocado --- cultivars --- preference mapping --- sensory evaluation --- sensory descriptive analysis --- consumer science --- unifloral honeys --- botanical origin --- physicochemical parameters --- classification --- natural language processing --- deep learning --- sensory science --- flavor lexicon --- long short-term memory --- sensory --- physicochemical measurements --- artificial neural networks --- near infra-red spectroscopy --- wine quality --- machine learning modeling --- weather --- consumer acceptance prediction --- data fusion --- emotion recognition --- facial expression recognition --- galvanic skin response --- machine learning --- neural networks --- sensory analysis --- avocado --- cultivars --- preference mapping --- sensory evaluation --- sensory descriptive analysis --- consumer science --- unifloral honeys --- botanical origin --- physicochemical parameters --- classification --- natural language processing --- deep learning --- sensory science --- flavor lexicon --- long short-term memory

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In the food and beverage industries, implementing novel methods using digital technologies such as artificial intelligence (AI), sensors, robotics, computer vision, machine learning (ML), and sensory analysis using augmented reality (AR) has become critical to maintaining and increasing the products’ quality traits and international competitiveness, especially within the past five years. Fermented beverages have been one of the most researched industries to implement these technologies to assess product composition and improve production processes and product quality. This Special Issue (SI) is focused on the latest research on the application of digital technologies on beverage fermentation monitoring and the improvement of processing performance, product quality and sensory acceptability.

Research & information: general --- Biology, life sciences --- Technology, engineering, agriculture --- sensor networks --- automation --- beer acceptability --- beer fermentation --- RoboBEER --- machine learning --- ultrasonic measurements --- long short-term memory --- industrial digital technologies --- yeast morphology --- automated image analysis --- heat stress --- vacuoles --- cell size --- computer vision --- foam stability --- image analysis --- lager beer --- foam retention --- polyphenols --- LC-ESI-QTOF-MS/MS --- HPLC --- medicinal plants --- ginger --- lemon --- mint --- herbal tea infusion --- antioxidants --- black pepper --- focus group --- hops --- Kawakawa --- off aromas --- gas sensors --- robotic pourer --- aroma thresholds --- climate change --- artificial neural networks --- volatile phenols --- glycoconjugates --- bushfires --- sparkling wine --- fermentation --- biogenic amines --- wine quality --- liquid chromatography --- principal component analysis --- augmented reality --- non-dairy yogurt --- contexts --- consumer acceptability --- emotional responses --- Fermentation --- Olea europaea --- respiration rate --- storage conditions --- transport --- TeeBot --- high throughput --- liquid handling robot --- metabolite analysis --- stochastic dynamic optimisation --- uncertainty --- n/a