Unmanned aerial vehicles (UAVs) equipped with lightweight sensors, such as RGB cameras and LiDAR, have significant potential in precision agriculture, including object detection. Tassel detection in maize is an essential trait given its relevance as the beginning of the reproductive stage of growth and development of the plants. However, compared with general object detection, tassel detection based on RGB imagery acquired by UAVs is more challenging due to the small size, time-dependent variable shape, and complexity of the objects of interest. A novel algorithm referred to as YOLOv5-tassel is proposed to detect tassels in UAV-based RGB imagery. A bidirectional feature pyramid network is adopted for the path-aggregation neck to effectively fuse cross-scale features. The robust attention module of SimAM is introduced to extract the features of interest before each detection head. An additional detection head is also introduced to improve small-size tassel detection based on the original YOLOv5. Annotation is performed with guidance from center points derived from CenterNet to improve the selection of the bounding boxes for tassels. Finally, to address the issue of limited reference data, transfer learning based on the VisDrone dataset is adopted. Testing results for our proposed YOLOv5-tassel method achieved the mAP value of 44.7%, which is better than well-known object detection approaches, such as FCOS, RetinaNet, and YOLOv5.

Community-based physical activity (PA) programs in Latin America have been recognized because of the use of available environmental resources to offer PA classes. Yet, the evaluation of programs focused on PA classes involving dancing in public spaces is limited. The aim of this study was to assess the physical activity levels, park use, and the contextual characteristics of public parks with and without the Recreovía in Bogotá in Colombia. Al Ritmo de las Comunidades is a natural experiment conducted in nine parks (3 parks implementing new Recreovías, 3 control parks and 3 parks with existing Recreovías) during 2013. We used the System for Observing Play and Recreation in Communities to evaluate park use (gender, age, and physical activity level) and target areas. A total of 4925 people were observed during 702 observation visits to parks. The percentage of women was higher in parks with Recreovía, compared to parks without Recreovía (53% vs. 40% vs. 33%; p < 0.001). Women using parks with Recreovía compared to women in parks without Recreovía were less likely to be sedentary (25% vs. 39%; p < 0.0001) and more likely to engage in moderate-to-vigorous activity (75% vs. 61%; p < 0.0001). Among men, the activity pattern was the opposite. The Recreovía is a promising strategy to promote park use and PA, especially among women who are less likely to meet PA recommendations during their leisure time. The provision of a cost-free community program may be an effective approach and a good investment for health.

Tassel counts provide valuable information related to flowering and yield prediction in maize, but are expensive and time-consuming to acquire via traditional manual approaches. High-resolution RGB imagery acquired by unmanned aerial vehicles (UAVs), coupled with advanced machine learning approaches, including deep learning (DL), provides a new capability for monitoring flowering. In this article, three state-of-the-art DL techniques, CenterNet based on point annotation, task-aware spatial disentanglement (TSD), and detecting objects with recursive feature pyramids and switchable atrous convolution (DetectoRS) based on bounding box annotation, are modified to improve their performance for this application and evaluated for tassel detection relative to Tasselnetv2+. The dataset for the experiments is comprised of RGB images of maize tassels from plant breeding experiments, which vary in size, complexity, and overlap. Results show that the point annotations are more accurate and simpler to acquire than the bounding boxes, and bounding box-based approaches are more sensitive to the size of the bounding boxes and background than point-based approaches. Overall, CenterNet has high accuracy in comparison to the other techniques, but DetectoRS can better detect early-stage tassels. The results for these experiments were more robust than Tasselnetv2+, which is sensitive to the number of tassels in the image.