Artificial intelligence-driven solutions for mitigating human–wildlife conflict in biodiversity hotspots

Simple item page
dc.contributor.authorOjija, Fredrick
dc.contributor.authorOgwu , Matthew C.
dc.date.accessioned2025-12-10T17:01:00Z
dc.date.available2025-12-10T17:01:00Z
dc.date.issued2025
dc.descriptionThis article was publish by SCIENCE PROGRESS
dc.description.abstractBiodiversity hotspots are biologically rich yet highly threatened regions that play a critical role in global conservation but often serve as epicentres of human–wildlife conflict (HWC). HWC poses major conservation and development challenges, undermining both human livelihoods and wildlife protection efforts. Artificial intelligence (AI) offers transformative tools for mitigating HWC by enhancing monitoring, prediction, and decision support. Through systematic searches of peer-reviewed and grey literature, this review analyzed 105 studies (1990–2025) from 163 screened sources, revealing that AI improved HWC monitoring (65%), predictive accuracy (47%), and community engagement (39%). AI-driven technologies such as machine learning, deep learning, and computer vision enable conservationists to process large datasets, automate species identification, and make real-time decisions. Integrated platforms like Earth Ranger and
dc.description.sponsorshipMUST
dc.identifier.otherDOI: 10.1177/00368504251394584
dc.identifier.urihttps://repository.must.ac.tz/handle/123456789/521
dc.language.isoen
dc.publisherSCIENCE PROGRESS
dc.titleArtificial intelligence-driven solutions for mitigating human–wildlife conflict in biodiversity hotspots
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
ojija-et-al-2025-artificial-intelligence-driven-solutions-for-mitigating-human-wildlife-conflict-in-biodiversity[1]-compressed.pdf
Size:
241.08 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Journal Articles