Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs
Abstract Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like developmental stage or health remain underexplor...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-78509-w |
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| _version_ | 1850194709412052992 |
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| author | Naresh Neupane Rhea Goswami Kyle Harrison Karen Oberhauser Leslie Ries Colin McCormick |
| author_facet | Naresh Neupane Rhea Goswami Kyle Harrison Karen Oberhauser Leslie Ries Colin McCormick |
| author_sort | Naresh Neupane |
| collection | DOAJ |
| description | Abstract Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like developmental stage or health remain underexplored or manually annotated, with limited focus on automating these features. As a proof-of-concept, we developed a computer vision model utilizing the YOLOv5 algorithm to accurately detect monarch butterfly caterpillars in photographs and classify them into their five developmental stages (instars). The training data were obtained from the iNaturalist portal, and the photographs were first classified and annotated by experts to allow supervised training of models. Our best trained model demonstrates excellent performance on object detection, achieving a mean average precision score of 95% across all five instars. In terms of classification, the YOLOv5l version yielded the best performance, reaching 87% instar classification accuracy for all classes in the test set. Our approach and model show promise in developing detection and classification models for developmental stages for insects, a resource that can be used for large-scale mechanistic studies. These photos hold valuable untapped information, and we’ve released our annotated collection as an open dataset to support replication and expansion of our methods. |
| format | Article |
| id | doaj-art-b69ebbb1ed2c42b1887a174a7483c846 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b69ebbb1ed2c42b1887a174a7483c8462025-08-20T02:13:55ZengNature PortfolioScientific Reports2045-23222024-11-0114111310.1038/s41598-024-78509-wArtificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographsNaresh Neupane0Rhea Goswami1Kyle Harrison2Karen Oberhauser3Leslie Ries4Colin McCormick5Georgetown UniversityGeorgetown UniversityGeorgetown UniversityUniversity of WisconsinGeorgetown UniversityGeorgetown UniversityAbstract Rapid technological advances and growing participation from amateur naturalists have made countless images of insects in their natural habitats available on global web portals. Despite advances in automated species identification, traits like developmental stage or health remain underexplored or manually annotated, with limited focus on automating these features. As a proof-of-concept, we developed a computer vision model utilizing the YOLOv5 algorithm to accurately detect monarch butterfly caterpillars in photographs and classify them into their five developmental stages (instars). The training data were obtained from the iNaturalist portal, and the photographs were first classified and annotated by experts to allow supervised training of models. Our best trained model demonstrates excellent performance on object detection, achieving a mean average precision score of 95% across all five instars. In terms of classification, the YOLOv5l version yielded the best performance, reaching 87% instar classification accuracy for all classes in the test set. Our approach and model show promise in developing detection and classification models for developmental stages for insects, a resource that can be used for large-scale mechanistic studies. These photos hold valuable untapped information, and we’ve released our annotated collection as an open dataset to support replication and expansion of our methods.https://doi.org/10.1038/s41598-024-78509-w |
| spellingShingle | Naresh Neupane Rhea Goswami Kyle Harrison Karen Oberhauser Leslie Ries Colin McCormick Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs Scientific Reports |
| title | Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs |
| title_full | Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs |
| title_fullStr | Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs |
| title_full_unstemmed | Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs |
| title_short | Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs |
| title_sort | artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs |
| url | https://doi.org/10.1038/s41598-024-78509-w |
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