Paper Contents
Abstract
This study presents the development of an intelligent image detection model designed to classify weather conditions using modern computer vision techniques. As technology continues to evolve, automated interpretation of environmental information has become essential for applications such as transportation safety, climate monitoring, smart agriculture, and outdoor activity planning. The proposed system leverages deep learning algorithmsparticularly convolutional neural networks (CNNs)to analyze visual features in weather images and accurately categorize conditions such as sunny, cloudy, rainy, and foggy environments. The model was trained on a diverse dataset of labeled weather images to ensure high accuracy and robust performance across varying lighting and environmental scenarios. Evaluation results demonstrate that the system achieves reliable classification, highlighting its potential integration into real-world intelligent systems. Overall, this research contributes to the advancement of computer vision by providing an efficient, automated approach to weather condition detection that supports smarter, data-driven technological solutions.
Copyright
Copyright © 2025 Olaiya Olayinka Oluwaseun, Aderanti Aderemi Taiwo, Moshood, Babatunde Ismail. This is an open access article distributed under the Creative Commons Attribution License.
