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The prominence of Edge Machine Learning is increasing swiftly as the performance of microcontrollers continues to improve. By deploying object detection and classification models on edge devices with camera sensors, it becomes possible to locate and identify objects in their vicinity. This technology finds valuable applications in wildlife conservation, particularly in camera traps used in African sanctuaries, and specifically in the Ngulia sanctuary, to monitor endangered species and provide early warnings for potential intruders. When an animal crosses the path of a an edge device equipped with a camera sensor, an image is captured, and the animal's presence and identity are subsequently determined. The performance of three distinct object detection models: SSD MobileNetV2, FOMO MobileNetV2, and YOLOv5 is evaluated. Furthermore, the compatibility of these models with three different microcontrollers ESP32 TimerCam from M5Stack, Sony Spresence, and LILYGO T-Camera S3 ESP32-S is explored. The deployment of Over-The-Air updates to edge devices stationed in remote areas is presented. It illustrates how an edge device, initially deployed with a model, can collect field data and be iteratively updated using an active learning pipeline. This project evaluates the performance of three different microcontrollers in conjunction with their respective camera sensors. A contribution of this work is a successful field deployment of a LILYGO T-Camera S3 ESP32-S running the FOMO MobileNetV2 model. The data captured by this setup fuels an active learning pipeline that can be iteratively retrain the FOMO MobileNetV2 model and update the LILYGO T-Camera S3 ESP32-S with new firmware through Over-The-Air updates. / Project Ngulia

Woodpeckers are a group of avian species that cause damage to wooden power utility structures. In the southeastern United States, Tennessee Valley Authority (TVA), has accrued an estimated $5 million USD annually from woodpecker damage. Previous work has focused on effectiveness of reactive mitigation and restoration efforts with little investigation of preventative methods. To address this knowledge gap, this study will i) use species distribution model techniques to predict damage suitability across the TVA service area, ii) use Bayesian hierarchical community model techniques to estimate species richness of the woodpecker community in the service area, and iii) recommend target areas for increased preventative measures in the service area. The suitability map indicated that damage was most likely to occur in the southwestern portions of the TVA service area. Woodpecker species richness was stable across the environmental covariate values estimated with 2-3 species found throughout the service area.

The Gulf of Mexico dune system is a broad and dynamic environment that varies greatly in geomorphology and vegetative composition across the Gulf coastline. Beach mice (Peromyscus polionotus spp.) are an endangered species that rely on coastal habitat structure. I hypothesized that beach mouse occupancy would be dependent upon coastal dune land cover and landform features. I identified coastal landforms using high-resolution elevation data and landform models in GRASS GIS and identified coastal dune vegetation classes using high-resolution aerial imagery and object oriented vegetation classification. These features were used to create a dynamic occupancy model to determine occupancy patterns in three subspecies of beach mice over multiple years of sampling. Beach mice demonstrated no distinct pattern in habitat occupancy over the study period. However, dynamic occupancy models demonstrated that habitat occupancy varied between individual sites, indicating that habitat selection may be population specific.

No description available.

No description available.

No description available.

No description available.