A structured literature review of computer vision methods for insect identification.

Automated image-based identification of adult insects is increasingly critical to biodiversity monitoring, pest management, and vector surveillance, yet practical deployment remains limited by data scarcity, field variability, and fine-grained taxonomic challenges. We conducted a PRISMA-guided literature review of computer-vision methods for insect classification and identification. A Web of Science Core Collection search (31 August 2024) retrieved 930 records; after deduplication (n = 2) and screening, 230 articles underwent full-text quality assessment using weighted criteria for taxonomy/methods, image capture, computational technique, sample size, and performance evaluation. Of these, 111 high-quality studies met inclusion thresholds. Data were extracted on taxonomic coverage, optical devices and experimental settings, algorithms and pipelines, datasets, and outcome metrics. Deep learning dominated the field; You Only Look Once variants were common for detection and ResNet/EfficientNet/MobileNet for classification; occasional hybrids combined Convolutional Neural Network (CNN) features with traditional classifiers. CNN-based and 1-stage detectors outperformed hand-crafted pipelines; transformers and self-supervised pre-training showed promise with limited labels. Despite strong laboratory performance, generalization to field conditions was hindered by illumination, occlusion, and pose variability. Public datasets were scarce and geographically skewed, limiting reproducibility and equitable benchmarking. Taxonomic coverage concentrated on Lepidoptera, Diptera, Hemiptera, and Coleoptera. We recommend advancing the field through comprehensive reporting beyond overall accuracy, the design of lifecycle-aware and domain-adapted models validated under field conditions, the establishment of diverse benchmarks with standardized imaging protocols, and the development of interpretable architectures suitable for deployment in embedded trapping systems.