Automatic image‐based identification and biomass estimation of invertebrates

Ärje, Johanna; Melvad, Claus; Jeppesen, Mads Rosenhøj; Madsen, Sigurd Agerskov; Raitoharju, Jenni; Rasmussen, Maria; Iosifidis, Alexandros; Tirronen, Ville; Gabbouj, Moncef; Meissner, Kristian; Høye, Toke T.

doi:10.1111/2041-210x.13428

Cited by 66 publications

(68 citation statements)

References 42 publications

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“…3. The BIODISCOVER machine can automate the process of invertebrate sample sorting, species identification, and biomass estimation (70). (A) The imaging system consists of an ethanol-filled spectroscopic cuvette, a powerful and adjustable light source, and two cameras capable of recording images at 50 frames per second (B) The setup is mounted in a light-proof aluminum box and fitted with a pump for refilling the spectroscopic cuvette.…”

Section: Potential Deep Learning Applications In Entomologymentioning

confidence: 99%

“…Legislative aquatic monitoring efforts in the United States and Europe require information about the abundance or biomass of individual taxa from bulk invertebrate samples. Using the BIODISCOVER machine, Ärje et al (70) were able to estimate biomass variation of individual specimens of Diptera species without destroying specimens. This was achieved from geometric features of the specimen extracted from images recorded by the BIODISCOVER machine and statistically relating such values to subsequently obtained dry mass from the same specimens.…”

Section: Potential Deep Learning Applications In Entomologymentioning

confidence: 99%

“…This can be done, for instance, by manually verifying the quality and labeling of images that are repeatedly misclassified by the deep learning methods. Standardized imaging devices such as the BIODISCOVER machine could also play a key role in building reference databases from monitoring programs (70). Training classifiers with species that are currently not encountered in a certain region but can possibly spread there later will naturally help to detect such changes when they occur.…”

Section: Future Directionsmentioning

confidence: 99%

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Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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246

188

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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.

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Section: Potential Deep Learning Applications In Entomologymentioning

confidence: 99%

Section: Potential Deep Learning Applications In Entomologymentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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246

188

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“…We developed and tested a camera trap for monitoring flower visiting insects, which records images at fixed intervals (45) controlling which specimens should be kept together for subsequent storage or analysis. The results for an initial dataset of images of 598 specimens across 12 species of known identity was very promising with a classification accuracy of 98.0% (73). The system is generic and can easily be used for other groups of invertebrates as well.…”

Section: Figurementioning

confidence: 94%

“…As such, the BIODISCOVER machine pave the way for cheap, fast, and accurate data on spatial and temporal variation in invertebrate abundance, diversity and biomass. Figure adapted with permission from (73).…”

Section: Figurementioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2020

Preprint

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ABSTRACTMost 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.

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Arthropod diversity in the alpine tundra using metabarcoding: Spatial and temporal differences in alpha‐ and beta‐diversity

Hein,

Astrin,

Beckers

et al. 2024

Ecology and Evolution

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All ecosystems face ecological challenges in this century. Therefore, it is becoming increasingly important to understand the ecology and degree of local adaptation of functionally important Arctic‐alpine biomes by looking at the most diverse taxon of metazoans: the Arthropoda. This is the first study to utilize metabarcoding in the Alpine tundra, providing insights into the effects of micro‐environmental parameters on alpha‐ and beta‐diversity of arthropods in such unique environments. To characterize arthropod diversity, pitfall traps were set at three middle‐alpine sampling sites in the Scandinavian mountain range in Norway during the snow‐free season in 2015. A metabarcoding approach was then used to determine the small‐scale biodiversity patterns of arthropods in the Alpine tundra. All DNA was extracted directly from the preservative EtOH from 27 pitfall traps. In order to identify the controlling environmental conditions, all sampling locations were equipped with automatic data loggers for permanent measurement of the microenvironmental conditions. The variables measured were: air temperature [°C] at 15 cm height, soil temperature [°C] at 15 cm depth, and soil moisture [vol.%] at 15 cm depth. A total of 233 Arthropoda OTUs were identified. The number of unique OTUs found per sampling location (ridge, south‐facing slope, and depression) was generally higher than the OTUs shared between the sampling locations, demonstrating that niche features greatly impact arthropod community structure. Our findings emphasize the fine‐scale heterogeneity of arctic–alpine ecosystems and provide evidence for trait‐based and niche‐driven adaptation. The spatial and temporal differences in arthropod diversity were best explained by soil moisture and soil temperature at the respective locations. Furthermore, our results show that arthropod diversity is underestimated in alpine‐tundra ecosystems using classical approaches and highlight the importance of integrating long‐term functional environmental data and modern taxonomic techniques into biodiversity research to expand our ecological understanding of fine‐ and meso‐scale biogeographical patterns.

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Automatic image‐based identification and biomass estimation of invertebrates

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 66 publications

References 42 publications

Deep learning and computer vision will transform entomology

Deep learning and computer vision will transform entomology

Deep learning and computer vision will transform entomology

Arthropod diversity in the alpine tundra using metabarcoding: Spatial and temporal differences in alpha‐ and beta‐diversity

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