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A case of a non-fatal crocodile attack on a human was reported on the banks of the Segovia River on the Honduras and Nicaragua border. The 12-year-old girl survived the attack, only with damage to the tibia of her right leg, where she lost part of the muscle tissue due to the bite. It was after the attack that the American crocodile was shot and killed by local people. In this paper, we discuss the possible causes of crocodile attacks. [ABSTRACT FROM AUTHOR]

Individual identification contributes significantly towards investigating behavioral mechanisms of animals and understanding underlying ecological principles. Most studies employ invasive procedures for individually identifying organisms. In recent times, computer-vision techniques have served as an alternative to invasive methods. However, these studies primarily rely on user input data collected from captivity or from individuals under partially restrained conditions. Challenges in collecting data from free-ranging individuals are higher when compared to captive populations. However, the former is a far more important priority for real-world applications. In this paper, we used Unmanned Aerial Vehicle (UAV) to collect data from free-ranging mugger crocodiles, Crocodylus palustris. We applied convolutional neural networks (CNNs) to individually identify muggers based on their dorsal scute patterns. The CNN model was trained on a data set of 88,000 images focusing on the mugger's dorsal body. The data was collected from 143 individuals across 19 different locations along the western part of India. We trained two CNN models, one with an annotated bounding box approach, the YOLO-v5l, and another without annotations, the Inception-v3. We used two parameters, True Positive Rate (TPR) and True Negative Rate (TNR), to validate the efficiency of the trained models. Using YOLO-v5l, TPR (re-identification of trained muggers) and TNR (differentiating untrained muggers as 'unknown') values at the 0.84 decision threshold were 88.8% and 89.6%, respectively. The trained model showed 100% TNR for the non-mugger species, the Gharial, Gavialis gangeticus , and the Saltwater crocodile, Crocodylus porosus. The performance of the CNN model was reliable and accurate while using only 125 images per individual for training purposes. Inception-v3 underperformed for both the parameters, thus, showing that a bounding box approach (YOLO-v5l model) with background elimination is a promising method to individually identify free-ranging mugger crocodiles. Our manuscript demonstrates that UAV imagery appears to be a promising tool for non-invasive collection of data from free-ranging populations. It can be used to train open-source algorithms for individual identification. Further, the identification method is entirely based upon dorsal scute patterns, which can be applied to different crocodilian species, as well. • Creation of annotated dataset for free-ranging mugger crocodiles using UAV. • Trained CNN models (YOLO-v5l and Inception-v3) for individual identification. • YOLO-v5l showed robust identification and re-identification of muggers. • Accurate detection of untrained, muggers and non-mugger species, as 'unknowns'. • Pre-trained CNN model will help ecologists to non-invasively tag focal muggers. [ABSTRACT FROM AUTHOR]