This research concerning the experience and future of zoos was carried out from 2011-2012 and takes regional ideas concerning Zoo Emmen as well as global visions into account. The research focuses partly on Zoo Emmen, its present attractions and visitors while also comparing and contrasting visions on the future in relationship to other international zoos in the world. In this way, remarkable experiences and ideas will be identified and in the light of them, it can serve as inspiration for stakeholders of zoos at large. The main research subject is a look at the future zoos in view of: The Zoo Experience – an international experience benchmark; The Zoo of the Future – a Scenario Planning approach towards the future; The virtual zoo - zoo’s in the internet domain.
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The transition towards a sustainable and healthy food system is one of the major sustainability challenges of today, next to the energy transition and the transition from a linear to circular economy. This paper provides a timely and evidence-based contribution to better understand the complex processes of institutional change and transformative social-ecological innovation that takes place in the food transition, through a case study of an open innovation and food transition network in The Netherlands, the South-Holland Food Family (Zuid-Hollandse Voedselfamilie). This network is supported by the provincial government and many partners, with the ambition to realize more sustainable agricultural and food chains, offering healthy, sustainable and affordable food for everyone in the Province of South-Holland in five to ten years from now. This ambition cannot be achieved through optimising the current food system. A transition is needed – a fundamental change of the food system’s structure, culture and practice. The Province has adopted a transition approach in its 2016 Innovation Agenda for Sustainable Agriculture. This paper provides an institutional analysis of how the transition approach has been established and developed in practice. Our main research question is what interventions and actions have shaped the transition approach and how does the dynamic interplay between actors and institutional structures influence institutional change, by analysing a series of closely related action situations and their context, looking at 'structure' and 'agency', and at the output-outcomes-impact of these action situations. For this purpose, we use the Transformative Social-Ecological Innovation (TSEI)-framework to study the dynamic interplay between actors and institutional structures influencing institutional change. The example of TSEI-framework application in this paper shows when and how local agents change the institutional context itself, which provides relevant insights on institutional work and the mutually constitutive nature of structure and agency. Above institutional analysis also shows the pivotal role of a number of actors, such as network facilitators and provincial minister, and their capability and skills to combine formal and informal institutional environments and logics and mobilize resources, thereby legitimizing and supporting the change effort. The results are indicative of the importance of institutional structures as both facilitating (i.e., the province’s policies) and limiting (e.g. land ownership) transition dynamics.
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This study presents an automated method for detecting and measuring the apex head thickness of tomato plants, a critical phenotypic trait associated with plant health, fruit development, and yield forecasting. Due to the apex's sensitivity to physical contact, non-invasive monitoring is essential. This paper addresses the demand for automated, contactless systems among Dutch growers. Our approach integrates deep learning models (YOLO and Faster RCNN) with RGB-D camera imaging to enable accurate, scalable, and non-invasive measurement in greenhouse environments. A dataset of 600 RGB-D images captured in a controlled greenhouse, was fully preprocessed, annotated, and augmented for optimal training. Experimental results show that YOLOv8n achieved superior performance with a precision of 91.2 %, recall of 86.7 %, and an Intersection over Union (IoU) score of 89.4 %. Other models, such as YOLOv9t, YOLOv10n, YOLOv11n, and Faster RCNN, demonstrated lower precision scores of 83.6 %, 74.6 %, 75.4 %, and 78 %, respectively. Their IoU scores were also lower, indicating less reliable detection. This research establishes a robust, real-time method for precision agriculture through automated apex head thickness measurement.
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