Deep learning and computer vision will transform entomology

Abstract

<jats:title>ABSTRACT</jats:title><jats:p>Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.</jats:p><jats:sec><jats:title>Significance statement</jats:title><jats:p>Insect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.</jats:p></jats:sec>

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

“…Overall, the proposed light trap and computer vision system showed promising results as a low-cost solution, which offers non-destructive and automatic monitoring of moths and classification of species. As such, the system provides novel opportunity for image-based insect monitoring of global relevance [ 42 ]. It should be considered as a viable alternative to traditional methods which typically requires tedious manual labor (i.e., visiting the trap several times in a season for observation) and often results in the killing of rare species of insects.…”

Insect decline in the Anthropocene: Death by a thousand cuts

“…Efforts are being made to standardize collection methods, establish data-collections norms, and improve data storage and accessibility (75,76). Insect traps with automated counting capacities are under development, some of which employ image-or sound-recognition technology to provide species-level data [e.g., Høye et al (77)]. At present, such automated monitoring can be used only for areas of modest insect diversity, where the insect fauna is relatively well known.…”

An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning

“…Overall, the proposed light trap and computer vision system showed promising results as a lowcost solution, which offers non-destructive and automatic monitoring of moths and classification of species. As such, the system provides novel opportunity for image-based insect monitoring of global relevance Høye et al (2020). It should be considered as a viable alternative to traditional methods which typically requires tedious manual labor (i.e.…”