Rapid estimation of insect biodiversity using AI assisted image analysis:a cost-effective tool for monitoring and ecological assessmen

Abstract

Protection of natural areas and environmentally friendly management of anthropogenic landscapes require reliable tools for monitoring and comparing biodiversity. Bioindicator-based approaches can support large-scale ecological assessment, but insect monitoring remains constrained by the taxonomic expertise required for species identification. Here, we propose an AI-supported index for estimating the biodiversity of flying insects, based on the diversity of visual features of insect bodies. Flying insects were captured using yellow sticky traps across agricultural and natural habitats, seasons, crops, and sites in Israel's Mediterranean region. Traps were scanned at high resolution and uploaded to a dedicated image database. A machine learning model was used to detect and delineate insects, after which engineered visual features representing body size, color, and shape were extracted. Biodiversity was estimated from distances among feature vectors and compared with taxonomic diversity derived from expert identification of a representative validation set. The AI-derived diversity index correlated strongly with expert-based taxonomic diversity, supporting the hypothesis that morphological variation in insect images can provide a proxy for taxonomic diversity. Applying the index to the full dataset revealed a negative relationship between insect abundance and diversity, while diversity also varied across habitats and sampling seasons. Our results demonstrate the potential of AI-based image analysis to provide rapid and cost-effective assessments of insect biodiversity without requiring complete taxonomic identification. This approach may facilitate biodiversity monitoring and support timely ecological assessment and decision-making in sustainable agricultural and landscape management.