The study presents a deep‑learning object detection framework that identifies products derived from threatened species—elephant, pangolin, and tiger—using real‑world images from online marketplaces, enforcement seizures, and market inspections. Trained on 39,941 images, the best model achieved 84.2% overall accuracy, with 93.6% for pangolin, 90.3% for tiger, and 71.1% for elephant. The optimized model was deployed in a prototype smartphone app, which reached 91.3% accuracy and an F‑score of 0.86 on an independent test set, demonstrating its potential for real‑time monitoring of wildlife trade.
arXiv:2606. 09353v1 Announce Type: cross Abstract: Individual animal recognition can be useful in the search for lost or stolen pets, the tracking of individuals of endangered species, and the recognition of animals in crowded farms.
arXiv:2610.15049v2 Announce Type: replace Abstract: This study evaluates and compares five convolutional neural network (CNN) architectures, AlexNet, Inception, LeNet, VGG16, and MobileNet, for diagn...
arXiv:2610.33243v1 Announce Type: new Abstract: Despite a ban on the international trading of recently poached ivory, elephants are still killed for their tusks. Ivory and its products are mainly sol...
arXiv:2608. 11053v1 Announce Type: cross Abstract: The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming.
arXiv:2606. 27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems.
arXiv:2606. 15655v1 Announce Type: new Abstract: The need for effective cattle identification technology is now more acutely felt than ever in maintaining biosecurity, food safety, and supply chain efficacy in livestock management.
arXiv:2603.04163v2 Announce Type: replace Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based...
arXiv:2606. 10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles.
arXiv:2606.20223v2 Announce Type: replace Abstract: Camera-trap monitoring in African tropical forests increasingly extends beyond closed-canopy interiors to riverbanks, clearings, and park edges. Am...
arXiv:2606. 14686v1 Announce Type: cross Abstract: Globally, cotton is a highly economically beneficial crop, as the textile industry heavily depends on it.
arXiv:2608.28161v1 Announce Type: cross Abstract: Mango variety identification in Bangladesh is challenging because closely related cultivars can have similar visual characteristics and images are of...
The paper introduces a new benchmark dataset of over 6,000 high‑resolution images for detecting and segmenting Western bluebirds in natural, cluttered scenes. Experiments show that supervised detectors such as Faster R‑CNN and RT‑DETR outperform open‑vocabulary models in zero‑shot settings, though fine‑tuned YOLO‑World can compete. Segmentation results favor Mask R‑CNN for mask quality, while YOLOv8‑Seg offers the best precision and speed. The study also identifies multiple factors—scale, brightness, contrast, clutter, blur, crowding, and session variation—as contributors to detection failures.