A Video Mosaicing-Based Sensing Method for Chicken Behavior Recognition on Edge Computing Devices.

Chicken behavior recognition is crucial for a number of reasons, including promoting animal welfare, ensuring the early detection of health issues, optimizing farm management practices, and contributing to more sustainable and ethical poultry farming. In this paper, we introduce a technique for recognizing chicken behavior on edge computing devices based on video sensing mosaicing. Our method combines video sensing mosaicing with deep learning to accurately identify specific chicken behaviors from videos. It attains remarkable accuracy, achieving 79.61% with MobileNetV2 for chickens demonstrating three types of behavior. These findings underscore the efficacy and promise of our approach in chicken behavior recognition on edge computing devices, making it adaptable for diverse applications. The ongoing exploration and identification of various behavioral patterns will contribute to a more comprehensive understanding of chicken behavior, enhancing the scope and accuracy of behavior analysis within diverse contexts.

High-resolution, spatially resolved quantification of wind erosion rates based on UAV images (case study: Sistan region, southeastern Iran).

Estimating the quantitative distribution of wind erosion rates is one of the most important requirements for managing affected environments and optimizing locations to control wind erosion. This study develops high-resolution maps for wind erosion with unmanned aerial vehicles or UAV images in the Sistan region-an area in southeastern Iran with severe wind erosion. Aerial imaging by UAV was done during period of erosive winds. Changes in the amount of wind erosion were measured for 7 months. Digital elevation models or DEM with a spatial resolution of 6 mm, orthophoto mosaic images with a resolution of 3 mm, were prepared before and after the erosion event. Three erosive facies consisting of surface, edge, and blowout were identified. The amount of erosion in different geomorphological landscapes or facies was measured according to differences of DEMs (DOD). The effect of physical factors of the geomorphological landscapes on wind erosion was investigated by calculating the correlation between the erosion, roughness, and slope in the geomorphological landscapes. The results showed that the highest and lowest mean of eroded soil were 22 mm and 4 mm in the blowout and surface facies, respectively. The average rate of wind erosion was 201 t/ha during the study period, which indicates the high intensity of wind erosion in the Sistan plain. Overall, UAV-as an aerial imaging technique collecting ground data-can be a helpful tool in the aeolian geomorphology especially for collecting data for measuring the rate of soil erosion by the wind in the aeolian landscapes located in remote regions.

[In vivo imaging of the corneal nerve plexus : From single image to large scale map]. / In-vivo-Bildgebung des kornealen Nervenplexus : Vom Einzelbild zur großflächigen Darstellung.

The sub-basal nerve plexus (SNP) of the cornea provides the possibility of in vivo and non-invasive examination of peripheral nerve structures by corneal confocal microscopy (CCM). Thus morphological alterations of the SNP can be directly detected and quantified. A single CCM image is insufficient for a well-founded diagnosis because of the inhomogeneous distribution of the nerve fibers; therefore, there is a demand for techniques for large area imaging of the SNP. This article provides an overview of published approaches to the problem. Current developmental work at the Karlsruhe Institute of Technology and the University of Rostock Eye Clinic is expected to lead to a simplified handling of the technology and a further improvement in the image quality.