High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics
Ecology documents and interprets the abundance and distribution of organisms. Ecoinformatics addresses this challenge by analyzing databases of observational data. Ecoinformatics of insects has high scientific and applied importance, as insects are abundant, speciose, and involved in many ecosystem...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fevo.2021.600931/full |
| _version_ | 1819087948898893824 |
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| author | Alexander Gerovichev Achiad Sadeh Vlad Winter Avi Bar-Massada Tamar Keasar Chen Keasar |
| author_facet | Alexander Gerovichev Achiad Sadeh Vlad Winter Avi Bar-Massada Tamar Keasar Chen Keasar |
| author_sort | Alexander Gerovichev |
| collection | DOAJ |
| description | Ecology documents and interprets the abundance and distribution of organisms. Ecoinformatics addresses this challenge by analyzing databases of observational data. Ecoinformatics of insects has high scientific and applied importance, as insects are abundant, speciose, and involved in many ecosystem functions. They also crucially impact human well-being, and human activities dramatically affect insect demography and phenology. Hazards, such as pollinator declines, outbreaks of agricultural pests and the spread insect-borne diseases, raise an urgent need to develop ecoinformatics strategies for their study. Yet, insect databases are mostly focused on a small number of pest species, as data acquisition is labor-intensive and requires taxonomical expertise. Thus, despite decades of research, we have only a qualitative notion regarding fundamental questions of insect ecology, and only limited knowledge about the spatio-temporal distribution of insects. We describe a novel high throughput cost-effective approach for monitoring flying insects as an enabling step toward “big data” entomology. The proposed approach combines “high tech” deep learning with “low tech” sticky traps that sample flying insects in diverse locations. As a proof of concept we considered three recent insect invaders of Israel’s forest ecosystem: two hemipteran pests of eucalypts and a parasitoid wasp that attacks one of them. We developed software, based on deep learning, to identify the three species in images of sticky traps from Eucalyptus forests. These image processing tasks are quite difficult as the insects are small (<5 mm) and stick to the traps in random poses. The resulting deep learning model discriminated the three focal organisms from one another, as well as from other elements such as leaves and other insects, with high precision. We used the model to compare the abundances of these species among six sites, and validated the results by manually counting insects on the traps. Having demonstrated the power of the proposed approach, we started a more ambitious study that monitors these insects at larger spatial and temporal scales. We aim at building an ecoinformatics repository for trap images and generating data-driven models of the populations’ dynamics and morphological traits. |
| first_indexed | 2024-12-21T21:44:16Z |
| format | Article |
| id | doaj.art-d2b91fd3dfc04e7bb95702be94c47199 |
| institution | Directory Open Access Journal |
| issn | 2296-701X |
| language | English |
| last_indexed | 2024-12-21T21:44:16Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Ecology and Evolution |
| spelling | doaj.art-d2b91fd3dfc04e7bb95702be94c471992022-12-21T18:49:17ZengFrontiers Media S.A.Frontiers in Ecology and Evolution2296-701X2021-05-01910.3389/fevo.2021.600931600931High Throughput Data Acquisition and Deep Learning for Insect EcoinformaticsAlexander Gerovichev0Achiad Sadeh1Vlad Winter2Avi Bar-Massada3Tamar Keasar4Chen Keasar5Department of Computer Science, Ben-Gurion University of the Negev, Beersheba, IsraelDepartment of Evolutionary and Environmental Biology, University of Haifa, Haifa, IsraelDepartment of Computer Science, Ben-Gurion University of the Negev, Beersheba, IsraelDepartment of Biology, University of Haifa – Oranim, Tivon, IsraelDepartment of Biology, University of Haifa – Oranim, Tivon, IsraelDepartment of Computer Science, Ben-Gurion University of the Negev, Beersheba, IsraelEcology documents and interprets the abundance and distribution of organisms. Ecoinformatics addresses this challenge by analyzing databases of observational data. Ecoinformatics of insects has high scientific and applied importance, as insects are abundant, speciose, and involved in many ecosystem functions. They also crucially impact human well-being, and human activities dramatically affect insect demography and phenology. Hazards, such as pollinator declines, outbreaks of agricultural pests and the spread insect-borne diseases, raise an urgent need to develop ecoinformatics strategies for their study. Yet, insect databases are mostly focused on a small number of pest species, as data acquisition is labor-intensive and requires taxonomical expertise. Thus, despite decades of research, we have only a qualitative notion regarding fundamental questions of insect ecology, and only limited knowledge about the spatio-temporal distribution of insects. We describe a novel high throughput cost-effective approach for monitoring flying insects as an enabling step toward “big data” entomology. The proposed approach combines “high tech” deep learning with “low tech” sticky traps that sample flying insects in diverse locations. As a proof of concept we considered three recent insect invaders of Israel’s forest ecosystem: two hemipteran pests of eucalypts and a parasitoid wasp that attacks one of them. We developed software, based on deep learning, to identify the three species in images of sticky traps from Eucalyptus forests. These image processing tasks are quite difficult as the insects are small (<5 mm) and stick to the traps in random poses. The resulting deep learning model discriminated the three focal organisms from one another, as well as from other elements such as leaves and other insects, with high precision. We used the model to compare the abundances of these species among six sites, and validated the results by manually counting insects on the traps. Having demonstrated the power of the proposed approach, we started a more ambitious study that monitors these insects at larger spatial and temporal scales. We aim at building an ecoinformatics repository for trap images and generating data-driven models of the populations’ dynamics and morphological traits.https://www.frontiersin.org/articles/10.3389/fevo.2021.600931/fullecoinformaticsimage classificationdeep learningpest controlinvasive insectsticky trap |
| spellingShingle | Alexander Gerovichev Achiad Sadeh Vlad Winter Avi Bar-Massada Tamar Keasar Chen Keasar High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics Frontiers in Ecology and Evolution ecoinformatics image classification deep learning pest control invasive insect sticky trap |
| title | High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics |
| title_full | High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics |
| title_fullStr | High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics |
| title_full_unstemmed | High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics |
| title_short | High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics |
| title_sort | high throughput data acquisition and deep learning for insect ecoinformatics |
| topic | ecoinformatics image classification deep learning pest control invasive insect sticky trap |
| url | https://www.frontiersin.org/articles/10.3389/fevo.2021.600931/full |
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