Weeds are one of the main factors affecting the yield and quality of agricultural products. Accurate evaluation of weed density is of great significance for field management, especially precision weeding. In this paper, a weed density calculating and mapping method in the field is proposed. An unmanned aerial vehicle (UAV) was used to capture field images. The excess green minus excess red index, combined with the minimum error threshold segmentation method, was used to segment green plants and bare land. A modified U-net was used to segment crops from images. After removing the bare land and crops from the field, images of weeds were obtained. The weed density was evaluated by the ratio of weed area to total area on the segmented image. The accuracy of the green plant segmentation was 93.5%. In terms of crop segmentation, the intersection over union (IoU) was 93.40%, and the segmentation time of a single image was 35.90 ms. Finally, the determination coefficient of the UAV evaluated weed density and the manually observed weed density was 0.94, and the root mean square error was 0.03. With the proposed method, the weed density of a field can be effectively evaluated from UAV images, hence providing critical information for precision weeding.

Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems.

Dehydration in older adults poses significant health risks, requiring effective monitoring solutions. This study addresses the challenge of detecting fluid intake accurately using a first-person, vision-based approach with wearable cameras and advanced object detection models. We developed a comprehensive dataset comprising 17 hours of drinking footage (∼3100 events) and 15 hours of nondrinking activities (∼3600 events) recorded as interference, from 36 participants, collected between October 2022 and January 2023 at King's College London. We include various container types and daily activities to enhance the model's robustness and generalizability. YOLOv8 models were used to detect drinking-related objects, and a mechanism was developed to analyse the size and position of the detection output to identify hand-container interactions and movements. The models achieved mAP@50 over 0.97 and F1-score over 0.95 in detecting drinking-related objects. Action detection testing results from video streams demonstrated an F1-score of 0.917, which dropped to 0.863 when interference activities were added. Additionally, the model detected the start of drinking activities with an average latency of 0.24 seconds and the end with 0.04 seconds, indicating high temporal accuracy. These results demonstrate the feasibility of egocentric, vision-based fluidintake detection and its potential application in preventing dehydration. To our knowledge, this is the first vision-based dataset focusing on fluid-intake actions from a first-person viewpoint—offering a novel foundation for advancing hydration monitoring in older adults and various real-world contexts.