Biodiversity and Abundance Analysis of Soil Arthropods By Using The Artificial Intelligence (AI)
Keywords:
Biodiversity, Land use systems Soil Arthopods, Diversity indices, AIAbstract
Soil arthropods play a crucial role in ecosystem functioning by contributing to decomposition, nutrient cycling, and soil structure maintenance. Understanding their biodiversity and abundance is essential for ecological monitoring and sustainable land management. Traditional methods for identifying and quantifying soil arthropods are time-consuming and require specialized taxonomic expertise. The integration of Artificial Intelligence (AI) in zoological research provides innovative solutions for rapid identification, classification, and biodiversity assessment. This study explores the application of AI techniques in analyzing soil arthropod biodiversity and abundance. Soil samples were collected using standard extraction techniques, and images of arthropods were processed using machine learning algorithms for automated classification. The results demonstrate that AI-based methods significantly improve the speed and accuracy of biodiversity assessment while reducing manual effort. The study highlights the potential of AI-driven tools in ecological monitoring and biodiversity conservation. In the current biodiversity crisis, the increasing demand for effective conservation tools aligns with significant advancements in artificial intelligence (AI). There is the need for the development of more robust and accurate forecasting methods, ultimately enhancing our understanding of ecological dynamics and supporting the formulation of effective conservation strategies. The present study confirmed that soil arthropods thrived generally more abundantly in undisturbed habitats (forests) than in cultivated and grasslands. This implies the need to step up conservation schemes in more representative sites in this ecosystem.
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