Biology subjects

Dorin, A.

Publications and source records attributed to Dorin, A..

3 recordsLinked to original sources

bioRxiv2022

A Framework for Better Sensor-Based Beehive Health Monitoring

Hive bees provide essential pollination services to human agriculture. Managed honey bees in particular pollinate many crops, but also create honey and other bee products that are now of global economic importance. Key aspects of honey bee behaviour can be understood by observing hives. Hence, the limitations of manual observation are increasingly being addressed by new technologies that automate and extend the reach of hive monitoring. Here we propose a framework to classify and clarify the potential for sensor-assisted hive monitoring to inform apiculture and, ultimately, improve hive bee management. This framework considers hive monitoring approaches across three newly proposed categories: Operational monitoring, Investigative monitoring, and Predictive monitoring. These categories constitute a new "OIP Framework" of hive monitoring. Each category has its own requirements for underlying technology that includes sensors and ICT resources we outline. Each category is associated with particular outcomes and benefits for apiculture and hive health monitoring detailed here. Application of these three classes of sensor-assisted hive monitoring can simplify understanding and improve best-practice management of hive bees. Our survey and classification of hive monitoring to date show that it is seldom practiced beyond honey bees, despite the need to understand bumble bees and stingless bees also. Perhaps unsurprisingly, sensor-based hive monitoring is shown to remain primarily a practice of developed nations. Yet we show how all countries, especially developing nations, stand to gain substantially from the benefits improved sensor-based hive monitoring offers. These include a better understanding of environmental change, an increased ability to manage pollination, an ability to respond rapidly to hive health issues such as pests and pathogens, and even an ability to react quickly to the danger posed to insects and humans alike by extreme events such as floods and fires. Finally, we anticipate that the future of hive monitoring lies in the application of Predictive monitoring, such that a hives anticipated future state can be preemptively managed by beekeepers working iteratively with novel hive monitoring technologies.

ecology↗

bioRxiv2021

Monitoring photogenic ecological phenomena: Social network site images reveal spatiotemporal phases of Japanese cherry blooms

Some ecological phenomena are visually engaging and widely celebrated. Consequently, these have the potential to generate large footprints in the online and social media image records which may be valuable for ecological research. Cherry tree blooms are one such event, especially in Japan where they are a cultural symbol (Sakura, ). For centuries, the Japanese have celebrated Hanami (flower viewing) and the historical data record of the festival allows for phenological studies over this period, one application of which is climate reconstruction. Here we analyse Flickr social network site data in an analogous way to reveal the cherry blossoms seasonal sweep from southern to northern Japan over a twelve-week period. Our method analyses data filtered using geographical constraints, multi-stage text-tag classification, and machine vision, to assess image content for relevance to our research question and use it to estimate historic cherry bloom times. We validated our estimated bloom times against official data, demonstrating the accuracy of the approach. We also investigated an out of season Autumn blooming that has gained worldwide media attention. Despite the complexity of human photographic and social media activity and the relatively small scale of this event, our method can reveal that this bloom has in fact been occurring over a decade. The approach we propose in our case study enables quick and effective monitoring of the photogenic spatiotemporal aspects of our rapidly changing world. It has the potential to be applied broadly to many ecological phenomena of widespread interest.

ecology↗

bioRxiv2020

Tracking individual honeybees among wildflower clusters with computer vision-facilitated pollinator monitoring

Monitoring animals in their natural habitat is essential for advancement of animal behavioural studies, especially in pollination studies. Non-invasive techniques are preferred for these purposes as they reduce opportunities for research apparatus to interfere with behaviour. One potentially valuable approach is image-based tracking. However, the complexity of tracking unmarked wild animals using video is challenging in uncontrolled outdoor environments. Out-of-the-box algorithms currently present several problems in this context that can compromise accuracy, especially in cases of occlusion in a 3D environment. To address the issue, we present a novel hybrid detection and tracking algorithm to monitor unmarked insects outdoors. Our software can detect an insect, identify when a tracked insect becomes occluded from view and when it re-emerges, determine when an insect exits the camera field of view, and our software assembles a series of insect locations into a coherent trajectory. The insect detecting component of the software uses background subtraction and deep learning-based detection together to accurately and efficiently locate the insect among a cluster of wildflowers. We applied our method to track honeybees foraging outdoors using a new dataset that includes complex background detail, wind-blown foliage, and insects moving into and out of occlusion beneath leaves and among three-dimensional plant structures. We evaluated our software against human observations and previous techniques. It tracked honeybees at a rate of 86.6% on our dataset, 43% higher than the computationally more expensive, standalone deep learning model YOLOv2. We illustrate the value of our approach to quantify fine-scale foraging of honeybees. The ability to track unmarked insect pollinators in this way will help researchers better understand pollination ecology. The increased efficiency of our hybrid approach paves the way for the application of deep learning-based techniques to animal tracking in real-time using low-powered devices suitable for continuous monitoring.

animal behavior and cognition↗