Abstract:

Biodiversity underpins the health of ecosystems and the services they provide to humanity. Despite its importance, biodiversity is in global decline, largely due to habitat destruction, climate change, pollution, and overexploitation. Accurate and timely assessment of biodiversity is critical for conservation efforts. However, traditional biodiversity assessment methods are time-consuming and often limited in scope. Artificial Intelligence (AI) offers a transformative approach by automating data collection and analysis processes. This paper examines how AI technologies are reshaping biodiversity hotspots and conservation. Artificial Intelligence (AI) is revolutionizing biodiversity monitoring by enabling large-scale, accurate, and real-time analysis of ecological data. This paper explores the current applications of AI in biodiversity assessment, including species identification, habitat mapping, and population dynamics monitoring. It highlights key tools such as computer vision, remote sensing, and deep learning, and discusses the challenges and ethical considerations involved. Finally, it outlines future directions for integrating AI in global conservation strategies. The assessment methodology includes Species Identification Convolutional Neural Networks (CNNs) are used to classify species from images or videos captured by camera traps, drones, or smartphones, Behavioural analysis in which AI models analyse movement patterns and interactions among species, AI-driven text extracts biodiversity data from scientific literature, field notes, and social media. AI is rapidly becoming an indispensable tool in biodiversity assessment, from automating species recognition to modelling habitat change.

Keywords:

Biodiversity, Hotspot, Artificial Intelligence, Assessment, Species,

Aim/Objectives

Methods