Revolutionizing biodiversity conservation in the mountainous regions: Harnessing the power of artificial intelligence and big data
ID: 613 / 435
Proposed Symposium Title: Revolutionizing biodiversity conservation in the mountainous regions: Harnessing the power of artificial intelligence and big data
Saddam Saqib1*, Jianfei Ye1*
Affiliations: 1School of Ecology, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China
Biodiversity conservation is a complex and pressing global challenge that requires effective management of vast amount of data. The emergence of big data and advancements in artificial intelligence (AI) offer unprecedented opportunities to revolutionize biodiversity conservation efforts. This study aims to explore the potential applications of AI in managing and analyzing big data for biodiversity conservation. We discuss the potential of AI and big data techniques in identifying threatened species, protection areas, habitat monitoring, species identification and distribution models to address conservation challenges and enhance decision-making processes. Our findings show that AI techniques can help in arranging and analyzing the species occurrence records, elevation data, and environmental factors by automatically extracting relevant information and generating standardize databases. AI techniques can help in species identification and classification through image recognition and machine learning, predictive modeling and habitat suitability mapping through machine learning algorithms, and data driven decision making by analyzing complex relationships within plant distribution data. Furthermore, AI techniques can guide detection of invasive species by analyzing remote sensing data, such as satellite imagery or unmanned aerial vehicle (UAV) data, shifts in plant distribution patterns such as habitat loss and threats to plant species. This study also highlighted future directions and opportunities of big data and AI, suggesting the need of data integration from different sources such as, real-time monitoring, designing predictive models and identifying genetic markers to suggest breeding programs for endangered species. This study suggests that harnessing AI techniques and big data can accurately explore biodiversity of the global hotspots of the world.