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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Privacy concerns can potentially make camera-based object classification unsuitable for robot navigation. To address this problem, we propose a novel object classification system using only a 2D-LiDAR sensor on mobile robots. The proposed system enables semantic understanding of the environment by applying the YOLOv8n model to classify objects such as tables, chairs, cupboards, walls, and door frames using only data captured by a 2D-LiDAR sensor. The experimental results show that the resulting YOLOv8n model achieved an accuracy of 83.7% in real-time classification running on Raspberry Pi 5, despite having a lower accuracy when classifying door-frames and walls. This validates our proposed approach as a privacy-friendly alternative to camera-based methods and illustrates that it can run on small computers onboard mobile robots.
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Agriculture and horticulture are essential for ensuring safe food to the growing global population, but they also contribute significantly to climate change and biodiversity loss due to the extensive use of chemicals. Integrated pest management is currently employed to monitor and control pest populations, but it relies on labor-intensive methods with low accuracy. Automating crop monitoring using aerial robotics, such as flapping-wing drones, presents a viable solution. This study explores the application of deep learning algorithms, You Only Look Once (YOLO) and Faster region-based convolutional neural network regions with convolutional neural networks (R-CNN), for pest and disease detection in greenhouse environments. The research involved collecting and annotating a diverse dataset of images and videos of common pests and diseases affecting tomatoes, bell peppers, and cucumbers cultivated in Dutch greenhouses. Data augmentation and image resizing techniques were applied to enhance the dataset. The study compared the performance of YOLO and Faster R-CNN, with YOLO demonstrating superior performance. Testing on data acquired by flapping-wing drones showed that YOLO could detect powdery mildew with accuracy ranging from 0.29 to 0.61 despite the shaking movement induced by the actuation system of the drone’s flapping wings.
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The paper introduced an automatic score detection model using object detection techniques. The performance of sevenmodels belonging to two different architectural setups was compared. Models like YOLOv8n, YOLOv8s, YOLOv8m, RetinaNet-50, and RetinaNet-101 are single-shot detectors, while Faster RCNN-50 and Faster RCNN-101 belong to the two-shot detectors category. The dataset was manually captured from the shooting range and expanded by generating more versatile data using Python code. Before the dataset was trained to develop models, it was resized (640x640) and augmented using Roboflow API. The trained models were then assessed on the test dataset, and their performance was compared using matrices like mAP50, mAP50-90, precision, and recall. The results showed that YOLOv8 models can detect multiple objects with good confidence scores.
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In de media is steeds meer aandacht voor armoede-en schuldenprobblematiek. Jongeren komen daarbij als doelgroep steeds vaker expliciet in beeld. Haet standard profiel van jongeren met schulden is vrijwel altijd hetzelfde: hoge telefoonrekeningen, excessieveuitgaven aan uitgaan en kleding. Maar klopt deze dominante beeldvorming wel? En in hoeverre dragen deze beelden bij aan het verminderen en oplossen van de schuldenproblematiek
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In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone footage to aid FRs in tasks such as locating victims. Detecting small-scale objects, particularly humans from high altitudes, poses a challenge for AI systems. We present first steps of introducing and evaluating a series of YOLOv8 Convolutional Neural Networks (CNNs) for human detection from drone images. The models are fine-tuned on a created drone image dataset of the Dutch Fire Services and were able to achieve a 53.1% F1-Score, identifying 439 out of 825 humans in the test dataset. These preliminary findings, validated by an incident commander, highlight the promising utility of these models. Ongoing efforts aim to further refine the models and explore additional technologies.
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Docentenhandleiding bij Standpunt, VMBO, deel 3 Leerwerkboek (2016). Docentenboek met antwoorden op de opdrachten en toelichtingen bij tal van vraagstellingen, didactische tips en toetsvragen.
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Filosoferen met kinderen wordt steeds meer gezien als een activiteit die bijdraagt aan democratische vorming. Democratie leren door filosoferen is een diepgaand onderzoek naar de praktijk van het filosoferen in het basisonderwijs. Op verschillende niveaus is het curriculum 'filosoferen met kinderen' onderzocht. Het onderzoek gaat in op de idealen, het idee achter het filosoferen, op de feitelijke vormgeving en uitvoering van het filosoferen en de door de leraren en kinderen zelf gerapporteerde leereffecten ten aanzien van hun denken, dialoog en het omgaan met verschillen. In vier scholen en zestien groepen zijn leraren en kinderen geobserveerd en via interviews en vragenlijsten bevraagd over hun doelen en motieven, praktijken, ervaringen en leereffecten. Het onderzoek laat zien hoe filosoferen met kinderen theoretisch en praktisch een bijdrage kan leveren aan democratische burgerschapsvorming. Het onderzoek is uitgevoerd als promotieonderzoek aan de Universiteit voor Humanistiek.
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Docentenhandleiding bij de uitgave Standpunt VMBO, deel 1, leerwerkboek
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Leermethode levensbeschouwing voor VMBO. Onderdeel van een driedelige serie. Dit deel1 bevat zes hoofdstukken: H. 1 De levensbeschouwelijke kijk H. 2 Uitingen van levensbeschouwing H. 3 Jodendom H. 4 Christendom H. 5 Wie ben ik? H. 6 Vriendschap en liefde Het betreft een Leerwerkboek.
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