Abstract: The rapid pace of climate change has altered the distribution of animal diseases, increased their frequency, and dispersed them over a larger geographic area. Rising temperatures, fluctuating humidity, and erratic rainfall patterns have increased the risk of illness in cows. These modifications have facilitated the growth of diseases and vectors. As a result, timely and accurate identification of these illnesses has become crucial for both food security and sustainable animal health management. To detect and classify animal diseases using visual data, this study proposes a diagnostic framework that utilizes machine learning approaches, with a focus on convolutional neural networks (CNNs) in conjunction with classification models, including ResNet, YOLOv5, and AltCLIP. By learning to distinguish between healthy and sick animals, the model enables prompt identification and treatment of sick animals. Merging data on disease detection with climate parameters, we make comparisons between them to get the best result and use this to generate advanced tools for detecting diseases. This informs us about potential risks. According to the results, machine learning-based diagnosis can improve disease detection's accuracy and efficiency while also providing important new information for climate adaptation strategies in cattle management. The optimal model will produce a graphical user interface (GUI) that displays environmental risk scores, diagnostic data, and recommendations for actions such as monitoring the situation, seeking immediate veterinary care, or verifying the animal's health.

Gehad K. Hussien, Mohamed H. Khafagy and Hussam M. Elbehiery. “Impact of Climate Change on Animal Diseases Based on Machine Learning”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161231

@article{Hussien2025,
title = {Impact of Climate Change on Animal Diseases Based on Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161231},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161231},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Gehad K. Hussien and Mohamed H. Khafagy and Hussam M. Elbehiery}
}