Deep Learning Prediction of Temperature-Dependent Activity in Desert Reptiles

Abstract

The reptiles that live in the desert are ectotherms, which means that their bodily processes, movements, diet, and circadian rhythms are significantly influenced by temperature changes in the environment. These changes affect the energy balance, thermoregulation of reptiles, and the way that they use habitat areas. It is vital to predict the activity of desert reptiles as part of their adaptation to temperature fluctuations in the environment. Unfortunately, traditional methods of monitoring desert reptiles are based on direct observations and require lots of time and efforts due to the nature of the environment and the duration of observations.

In this study, a novel method of predicting reptile activities depending on temperature changes will be presented using deep learning algorithms, computer vision, and behavioural analysis technologies. The proposed framework uses data on reptile images and videos together with temperature information for identifying the behavioural patterns and predicting reptile activities under temperature changes. The deep learning algorithm will be used to automatically extract visual features and predict the reptile activities. The accuracy and efficiency of the model will be estimated with the help of classification metrics.

The experimental analysis has shown that the suggested method has proven its ability to detect temperature-induced changes in the animal's activity and achieve higher accuracy of predictions than the traditional approach to monitoring. The developed method represents a solution for automated, scalable, and non-invasive monitoring of reptiles. This research contributes to the field of wildlife ecology by introducing the tool for analyzing the behavior of reptiles due to changes in temperature