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Abstract: Introduction: Vending machines offer a convenient way for food distribution, particularly favored by employees, students, and individuals seeking a quick snack. Food vending machines typically offer unhealthy, calorie-dense, and nutrient-poor options, which contribute to the rise of non-communicable diseases. Creating a healthier food environment is crucial, particularly in universities where students are developing their eating habits and becoming more independent. Key considerations for vending machines include the quality, nutritional value, and price of the products with a recent and growing attention toward sustainability. Methods: The present study thoroughly examined 30 vending machines across 30 faculties in Slovenia. The analysis focused on assessing the variety and sustainability of the available products. The following was evaluated through three primary criteria, based on the information available on the product label: nutritional quality, environmental impact (palm oil content, packaging materials, and sustainability certificates), and socioeconomic indicators (suitability for people with special dietary needs). Results: The results revealed a low proportion of products met the proposed sustainability criteria, highlighting the need to promote sustainability in the vending machine industry. Although food categories like dairy products, fruits, and nuts have better nutritional profiles, they are underrepresented. In contrast, items like biscuits, crisps, snacks, and pre-prepared sandwiches often exceed recommended fat, salt, and sugar levels. More than one-quarter of products contained palm oil, only two were labeled as palm oil-free, and a limited proportion of products were suitable for individuals with special dietary requirements such as gluten sensitivity and lactose intolerance. Discussion: Improving the food selection in vending machines, guided by suggested sustainability criteria, presents a promising strategy for reshaping the food environment and promoting sustainable healthy diets, taking into account nutritional, environmental, and socioeconomic indicators.
Keywords: vending machines, university, food nutritional quality, sustainability, environmental impact
Published in DKUM: 02.07.2025; Views: 0; Downloads: 3
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Abstract: The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).
Keywords: pig, welfare, image processing, object detection, deep learning, smart farming
Published in DKUM: 11.07.2024; Views: 87; Downloads: 9
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