ArticlePloS one2021
Automatic ladybird beetle detection using deep-learning models.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 24 citations in OpenAlex.
- Multi-angle, cross-domain fusion strategy enhances automated insect identification and hierarchical categorization: a case study on assassin bugs (Hemiptera: Reduviidae).Cladistics : the international journal of the Willi Hennig Society · 2026Article
- Artificial intelligence correctly classifies developmental stages of monarch caterpillars enabling better conservation through the use of community science photographs.Scientific reports · 2024Article
- Detecting common coccinellids found in sorghum using deep learning models.Scientific reports · 2023Article
- Artificial intelligence versus natural selection: Using computer vision techniques to classify bees and bee mimics.iScience · 2022Article
- Edge Computing for Vision-Based, Urban-Insects Traps in the Context of Smart Cities.Sensors (Basel, Switzerland) · 2022Article
- An automated pipeline for supervised classification of petal color from citizen science photographs.Applications in plant sciencesArticle
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Authors and funding
9 authors at 3 institutions in 3 countries.
Funding
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Abstract
Fast and accurate taxonomic identification of invasive trans-located ladybird beetle species is essential to prevent significant impacts on biological communities, ecosystem functions, and agricultural business economics. Therefore, in this work we propose a two-step automatic detector for ladybird beetles in random environment images as the first stage towards an automated classification system. First, an image processing module composed of a saliency map representation, simple linear iterative clustering superpixels segmentation, and active contour methods allowed us to generate bounding boxes with possible ladybird beetles locations within an image. Subsequently, a deep convolutional neural network-based classifier selects only the bounding boxes with ladybird beetles as the final output. This method was validated on a 2, 300 ladybird beetle image data set from Ecuador and Colombia obtained from the iNaturalist project. The proposed approach achieved an accuracy score of 92% and an area under the receiver operating characteristic curve of 0.977 for the bounding box generation and classification tasks. These successful results enable the proposed detector as a valuable tool for helping specialists in the ladybird beetle detection problem.
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