Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision Gálvez Salido, Aarón Robles Rodríguez, Francisca Gonçalves, Rodrigo Javier Herrán Moreno, Roberto De La Ruiz Rejón, Carmelo Navajas Pérez, Rafael Deep Learning Computer vision Automated counting Automated biological counting is essential for scaling wildlife monitoring and biodiversity assessments, as manual processing currently limits analytical effort and scalability. This review evaluates the integration of deep learning and computer vision across diverse acquisition platforms, including camera traps, unmanned aerial vehicles (UAVs), and remote sensing. Methodological paradigms ranging from Convolutional Neural Networks (CNNs) and one-stage detectors like You Only Look Once (YOLO) to recent transformer-based architectures and hybrid models are examined. The literature shows that these methods consistently achieve high accuracy—often exceeding 95%—across various taxa, including insect pests, aquatic organisms, terrestrial vegetation, and forest ecosystems. However, persistent challenges such as object occlusion, cryptic species differentiation, and the scarcity of high-quality, labeled datasets continue to hinder fully automated workflows. We conclude that while automated counting has fundamentally increased data throughput, future advancements must focus on enhancing model generalization through self-supervised learning and improved data augmentation techniques. These developments are critical for transitioning from experimental models to robust, operational tools for global ecological monitoring and conservation efforts. 2026-02-19T08:38:49Z 2026-02-19T08:38:49Z 2026-02-18 journal article Gálvez-Salido, A., Robles, F., Gonçalves, R. J., Herrán, R. d. l., Ruiz Rejón, C., & Navajas-Pérez, R. (2026). Analysis of Biological Images and Quantitative Monitoring Using Deep Learning and Computer Vision. Journal of Imaging, 12(2), 88. https://doi.org/10.3390/jimaging12020088 https://hdl.handle.net/10481/111232 10.3390/jimaging12020088 eng info:eu-repo/grantAgreement/EU/PRTR/10.13039/501100011033 http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI