Deep learning and computer vision will transform entomology
| Autor: | Johanna Ärje, Kristian Meissner, Kim Bjerge, Toke T. Høye, Claus Melvad, Alexandros Iosifidis, Florian Leese, Hjalte M. R. Mann, Jenni Raitoharju, Oskar Liset Pryds Hansen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Entomology Insecta Computer science Ecology (disciplines) media_common.quotation_subject Ecological Parameter Monitoring Variation (game tree) Insect 010603 evolutionary biology 01 natural sciences 03 medical and health sciences Deep Learning Animals Computer vision Monitoring methods Animal species 030304 developmental biology Invertebrate media_common 0303 health sciences Multidisciplinary Training set business.industry Deep learning The Global Decline of Insects in the Anthropocene Special Feature Identification (information) Artificial intelligence business Biologie |
| Zdroj: | Høye, T T, Ärje, J, Bjerge, K, Hansen, O L P, Iosifidis, A, Leese, F, Mann, H M R, Meissner, K, Melvad, C & Raitoharju, J 2021, ' Deep learning and computer vision will transform entomology ', Proceedings of the National Academy of Sciences of the United States of America, vol. 118, no. 2, e2002545117 . https://doi.org/10.1073/pnas.2002545117 Proc Natl Acad Sci U S A |
| DOI: | 10.1101/2020.07.03.187252 |
| Popis: | 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.Significance statementInsect 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|