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Insect DX Lab
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A lab that uses digital technologies to solve social problems related to insects.
AboutAbout the lab
Solving insect-related social problems facing modern society
The Insect DX Lab uses digital technologies to aim for a transformation of business models in response to insect-related social problems facing modern society.
Its work covers automating insect species identification, adding higher value to insect resources by digitizing specimen data, assessing insect diversity using DX, and promoting DX through collaboration with insect science-related fields.
As a short-term goal, the lab aims to automate outbreak forecasting for stable flies and Calliphora nigribarbis using deep learning-based object detection, leading to prompt and appropriate pest control.
As a long-term goal, it aims to infer how biodiversity will change under climate change, using data such as distribution data from insect specimen collections.
Key terms
- Deep learning
- A machine learning method in which computers learn features by themselves from large amounts of data.
- Outbreak forecasting
- Efforts to anticipate pest outbreaks in advance. It is important in integrated pest management.
- Potential distribution range
- The range in which an insect is expected to be able to occur, estimated from specimen distribution data.
ResearchResearch themes
The lab uses digital technologies to aim for a transformation of business models in response to insect-related social problems facing modern society. Through automating insect species identification, digitizing specimen data, and assessing insect diversity, it sets both challenges that can be achieved in the short term and challenges that should be achieved in the long term.
*Illustrative imageAutomating Fly Outbreak Forecasting
The lab aims to automate outbreak forecasting using deep learning-based object detection, leading to prompt and appropriate pest control. The targets are stable flies, which cause social losses by mechanically transmitting bovine leukemia virus and by stressing cattle, and Calliphora nigribarbis, which is highly likely to mechanically transmit highly pathogenic avian influenza virus.
*Illustrative imageInsect Diversity Seen Through Specimen Data
The lab estimates potential distribution ranges from data such as distribution data obtained from Kyushu University's insect specimen collection, and infers changes in biodiversity by incorporating climate change models. It also aims to advance insect taxonomy by building 3D models of specimens and using unsupervised classification and heat maps.
MembersFaculty
- ProfessorSatoshi Kamitani
- Assistant ProfessorSadahisa Yagi
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Aid to the identification of insects (Plate ) Percosoma sculcipenne (cropped)” Waterhouse, Charles Owen / Public domain (cropped)
- Automating Fly Outbreak Forecasting: “Natural fly trap Philippines 001” Martin Michlmayr / CC BY-SA 4.0 (cropped)
- Insect Diversity Seen Through Specimen Data: “Insect collections Natural History Museum Dublin 02” Smirkybec / CC BY-SA 4.0 (cropped)
- Related lab (昆虫食科学): “Cricket Shelter Modular Edible Insect Farm IMG” Mary Mark Ockerbloom / CC BY-SA 4.0 (cropped)
- Related lab (天敵微生物学): “Beetle Beauveria cf 2026 Südpfalz” InsektenDoku / CC BY-SA 4.0 (cropped)
- Related lab (昆虫産業創生学): “CSIRO ScienceImage 11069 Equipment at the NCRIS Reombinant Protein Production Facility” Morgan Brown, CSIRO / CC BY 3.0 (cropped)
- Related lab (生物保護管理学): “APHIS-Moore Air Base in Texas-Plant Protection and Quarantine Science and Technology Insect Management and Molecular Diagnostics Laboratory (20230621-CDP-APHIS-0277)” USDAgov / Public domain (cropped)
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