Academic Journal
Improving wildlife track classification through human-in-the-loop method and explainable AI.
| Τίτλος: | Improving wildlife track classification through human-in-the-loop method and explainable AI. |
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| Συγγραφείς: | Petso T; Department of Mechanical, Energy and Industrial Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana. tinaopetso@gmail.com., Jamisola RS Jr; Department of Mechanical, Energy and Industrial Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana., Alibhai S; WildTrack Inc., Durham, NC, USA.; Duke University, Nicholas School of the Environment, Durham, NC, USA., Lebopo M; Khama Rhino Sanctuary, P O Box 10, Serowe, Botswana., Peter K; Botswana Defence Force, Private Bag X06, Gaborone, Botswana., Namoshe M; Department of Mechanical, Energy and Industrial Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana., Mmereki W; Department of Mechanical, Energy and Industrial Engineering, Botswana International University of Science and Technology, Private Bag 16, Palapye, Botswana., Jewell Z; WildTrack Inc., Durham, NC, USA.; Duke University, Nicholas School of the Environment, Durham, NC, USA. |
| Πηγή: | Scientific reports [Sci Rep] 2026 Apr 20; Vol. 16 (1). Date of Electronic Publication: 2026 Apr 20. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| Ιατρικοί όροι (MeSH): | Animals, Wild*/classification , Artificial Intelligence* , Classification Algorithms*, Image Processing, Computer-Assisted/methods ; Animals ; Humans |
| Περίληψη: | In this study, we present the integration of tracker expertise with artificial intelligence (AI) for wildlife species classification and its application to human-in-the-loop with an investigation in explainable AI. We collected images of wildlife tracks, built AI models from the track images, classified species based on the best performing model, and expert trackers evaluated the results against the AI model. The wildlife species included black rhinoceros (Diceros bicornis), blue wildebeest (Connochaetes taurinus), giraffe (Giraffa camelopardalis), and white rhinoceros (Ceratotherium simum). Two expert trackers and one non-expert tracker ranked the image quality of 3039 tracks. We then trained the AI model with different number of training images per class using different hyperparameter settings. The best-performing AI model was chosen and evaluated. Afterwards, 36 expert trackers evaluated the resulting model: one set using raw images only, and another set using raw with heatmap images. For the same hyperparameter settings with the best model performance evaluation, our method considerably increased the mean average precision@50-95 by 10.42% against a non-expert tracker. In addition, the required number of images for model training can be reduced by 25% when using inputs from a highly skilled expert tracker. The visual heatmaps provided a means in performing explainable AI by visually presenting the features of the tracks based on colours that help to guide the expert tracker evaluation. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Competing interests: The authors declare no competing interests. |
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| Grant Information: | W911NF- 23-1-0293 U.S. Army |
| Contributed Indexing: | Keywords: Artificial intelligence models; Explainable AI; Human-in-the-loop; Species classification; Wildlife tracks |
| Entry Date(s): | Date Created: 20260420 Date Completed: 20260614 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13265934 |
| DOI: | 10.1038/s41598-026-48229-4 |
| PMID: | 42009782 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2045-2322 |
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| DOI: | 10.1038/s41598-026-48229-4 |