Abstract
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 sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. 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, behavior, 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 exceptionally large datasets 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.
Highlights
We are experiencing a mass extinction of species [1], but data on changes in species diversity and abundance have substantial taxonomic, spatial, and temporal biases and gaps [2, 3]
Deep learning models designed for dealing with images, so-called convolutional neural networks (CNNs), can extract features from images or objects within them and learn to differentiate among them
Deep learning is currently influencing a wide range of scientific disciplines [85] but has only just begun to benefit entomology
Summary
The physical appearance of specimens can be captured by automated imaging in the laboratory When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. We argue that deep learning and computer vision can be used to develop novel high-throughput systems for detection, enumeration, classification, and discovery of species as well as for deriving functional traits such as biomass for biomonitoring purposes These approaches can help solve long standing challenges in ecology and biodiversity research and address pressing issues in insect population monitoring [32, 33]. We have recently demonstrated that our custom-built time-lapse cameras can record image data from which a deep learning model can accurately estimate local spatial, diurnal, and seasonal dynamics of honeybees and other flowervisiting insects [45] (Fig. 1). Harmonic scanning radars can detect insects flying at low altitudes at a range of several hundred meters, but insects need to be tagged with a radar transponder and must be within line of sight
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