Purpose: This study presents the concept of digital twin technology for the digitalization of tourism product competitiveness promotion recommendations.Design/methodology/approach: A qualitative research method was applied, conducting pilot interviews with representatives of the tourism industry in order to evaluate the key performance indicators (KPIs) and ecosystem specific to the tourism product category “food service”. Based on expert evaluations, a recommendation for promoting the competitiveness of the tourism product was created. The expert evaluations and the provided recommendations were used to create a model of the digital twin for the promotion of tourism competitiveness.Findings: The study found that it is possible to digitize tourism experts' knowledge into a format compatible with recommendation system development to create a digital twin to promote the competitiveness of the tourism industry.Originality/value: Currently, there is almost no research on the use of digital twins in the tourism industry. The application of this methodology in practice can ensure the promotion of the competitiveness of the tourism sector.

This research was conducted within the framework of a research project aimed at detecting patterns of plate waste and developing recommendations for improving catering in seven schools in Rezekne city (Latvia) by a combination of observation, physical weighing, semi-structured interview approaches and statistical analysis of variance (ANOVA). We identified plate waste (including wasted beverages), which remains after the lunch of schoolchildren in grades 1–7, examining a total of 7064 lunch samples. The originality of the research is due to the fact that a unified menu was designed for the field study, which ensured the same field study conditions in all the schools. The results of the research revealed that the average weight of plate waste per schoolchild reached 178 g, and the total weight of plate waste accounted for 28.75% of the total weight of food served. No significant differences in plate waste weight between various age groups and grades of schoolchildren were found, which was also confirmed by a one-way ANOVA test. An analysis of plate waste by food category showed that beverages accounted for the largest share of total plate waste (42.24%), followed by staple food (28.38%) and meat (11.77%). An analysis of plate waste shares of food served (%) by food category revealed a similar situation: the largest share of food served was made up of beverages (37.56%), followed by staple food (36.48%) and meat (28.77%). An analysis of the monetary value of food waste showed that the average cost of plate waste (excluding beverage) per schoolchild was EUR 0.236, which represented 16.6% of the national and municipal funding of EUR 1.42 per portion. Given the research results, the authors have concluded that in order to reduce the amount of plate waste generated by Rezekne city schools, school menus should be based not only on the requirements prescribed by relevant legal acts but also on cooking processes that meet the requirements of modern consumers (learners), e.g., by following trends in cooking practices in society to make the learners interested in consuming school food.

Food waste is a global problem, which becomes apparent at various stages of the food supply chain. The present research study focuses on the optimization of food consumption in schools and effective food management through data-driven decision making within the trends: zero food waste and digital transformation. The paper presents a plate waste forecasting system based on mathematical modeling and simulation using the Monte Carlo method, which showed an RMSE equal to ±3% and a MAPE of 10.15%. The solution based on the simulator provides a possibility to better understand the relationship between the parameters investigated through data visualization and apply this knowledge to train managers to make decisions that are more effective. The developed system has multi-disciplinary application: forecasting, education and decision making targeted to reduce food waste and improve public health and food management in schools.

In the context of precision horticulture, decision support tools play a significant role in providing fruit growers with insights into orchard conditions, facilitating informed decisions regarding orchard management practices. This study presents the development of an autonomous yield estimation system designed to provide decision support to small commercial orchards. Autonomous yield estimation is based on the application of UAVs and AI. AI is used to identify and quantify fruitlets and fruits in photographs collected by UAV. In this article, we present our prototype of an autonomous yield estimation system. The adapted “4+1” architecture was applied to design a system with a holistic approach analyzing software, hardware, and ecosystem requirements. Six datasets are presented, which contain the images of fruitlets and fruits of apples, pears, and cherries. Three CNN models were trained: YOLOv8m, YOLOv9m, and YOLOv10m. The experiment showed that the most accurate was YOLOv9m, which achieved mean accuracies of 0.896 mAP@50 and 0.510 mAP@50:95 for all datasets.

Food waste (FW) threatens food security, environmental sustainability, and economic efficiency, with about one-third of global food production lost or wasted. Schools play a crucial role in addressing FW, representing lost resources and missed educational opportunities. The present research assessed three interventions to reduce plate waste (PW) in Rezekne City schools, namely (S1) a plate waste tracker, (S2) an awareness and educational campaign, and (S3) organizational changes, including larger plates, extended lunch breaks, and teacher supervision. Implemented in three schools with a fourth as a control, PW was measured at three intervals, at pre-intervention, short-term, and long-term post-intervention. The PW data analysis utilized two models (day view and class view) and a Wilcoxon signed-rank test. While the plate waste tracker initially reduced PW, sustained impact required continuous reinforcement. The awareness and educational campaign alone proved insufficient, highlighting the need for complex strategies. The organizational changes unexpectedly increased PW, underscoring FW’s complexity. The research has concluded that reducing FW requires tailored and multi-faceted approaches. According to the MOA framework, the school catering model in Rezekne City lacks essential “Opportunities” for effective FW reduction, as students have limited flexibility in portion sizes and food choices, which hinders the interventions’ effectiveness. Future research should explore adaptable FW-reducing interventions suited to specific school contexts.