Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

Abstract

In conservation, the use of unmanned aerial vehicles (drones) carrying various sensors and the use of deep learning are increasing, but they are typically used independently of each other. Untapping their large potential requires integrating these tools. We combine drone-borne thermal imaging with artificial intelligence to locate ground-nests of birds on agricultural land. We show, for the first time, that this semi-automated system can identify nests with a high performance. However, local weather, type of arable field and height of the drone can affect performance. The results’ implications are particularly relevant to conservation practitioners working across sectors, such as biodiversity conservation and food production in farmland. Under a rapidly changing world, studies like this can help uncover the potential of technology for conservation and embrace cross-sectoral transformations from the onset; for example, by integrating nest detection within the precision agriculture system that heavily relies on drone-borne sensors.

Using UAV‐mounted thermal cameras to detect the presence of nesting nightjar in upland clear‐fell: A case study in South Wales, UK

“…Nightjars are difficult to study because they are cryptic and nocturnal, and nests are difficult to find even if you are standing next to them (Troscianko et al., 2016). Traditional techniques for locating nests are time consuming, labour intensive, potentially disturbing to the birds and may be affected by an observer bias (Hodgson et al., 2018; Santangeli et al., 2020). This study confirms that UAV‐mounted thermal cameras can locate cryptic ground‐nesting birds over small to medium (∼50 ha) survey areas.…”

“…This has been shown to be successful in the detection of thermal signatures of animals in previous UAV survey work and would be recommended for future applications (Lhoest et al, 2015;Longmore et al, 2017;Santangeli et al, 2020).…”

“…radio tags: Alexander & Cresswell, 1990; thermal imagery: Boonstra et al., 1995; Boulton & Cassey, 2012; Galligan et al., 2003; McCafferty et al., 1998; and recently the use of unmanned aerial vehicles [UAVs]). UAV‐mounted thermal sensors can monitor the heat radiation of visually cryptic endothermic animals to identify their location (Bushaw et al., 2020; Israel & Reinhard, 2017; Santangeli et al., 2020; Seymour et al., 2017; Witczuk et al., 2017; ). UAVs can provide a means of rapidly surveying large areas using thermal sensors and may represent a more cost‐effective less disturbing alternative to traditional research methods (Bushaw et al., 2020; Christie et al., 2016; Santangeli et al., 2020).…”

“…Detection probability can be affected by the time of day but also by the ambient conditions and flight characteristics – speed and flight altitude (Witczuk et al., 2018). The probability of false absences is also known to be more likely when images are taken from further above ground level (Santangeli et al., 2020), suggesting flight height above the ground is a key consideration in conservation applications.…”

The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials

“…Imaging sensors are a common UAS payload and can range from standard RGB frame or video cameras up to multi-and hyperspectral imaging sensors, which allow the mapping of chemical and physical properties of the observed target. Current developments in UAS technology look to increase flight times, payload, and ease of operation, and artificial intelligence (AI) has a key role in this goal [15], [16]. Parallel to the technical development, the number of prospective users and application fields for drone-borne mapping rises fast, including mineral exploration, mining, and environmental monitoring due to, for example, the possibility of acquiring data with high spatial resolution covering extensive regions and even reaching complicated terrains.…”