<p>The increasing frequency and severity of wildfires worldwide have led to a pressing need for advanced fire detection systems. Recent research has demonstrated the effectiveness of deep learning models, particularly Convolutional Neural Networks (CNNs), in wildfire detection using RGB images. However, the reliance on single color space limits the ability of fire detection models to generalize across diverse environmental conditions. This raises the question of why it is important to explore multiple color spaces: different color representations can capture distinct features of fire that are not visible in RGB alone, thereby improving detection robustness in varying environments. To answer this, we propose PyroChroma, an enhanced version of the MobileNetV2 architecture that incorporates additional convolution blocks and multiple color spaces (RGB, HSV, YCbCr, and grayscale) to capture a wider range of fire characteristics. We conduct extensive experiments to evaluate the performance of PyroChroma against state-of-the-art models, employing transfer learning techniques to leverage pre-trained models in resource-constrained environments. Our results demonstrate that PyroChroma consistently outperforms existing models, achieving an accuracy of 99% and surpassing established architectures such as VGG16 (97%), ResNet50v2 (97%), and Xception (98%) in key metrics such as precision, recall, and F1 score, even with limited training data. Furthermore, we integrate Local Interpretable Model-agnostic Explanations (LIME) into PyroChroma to provide visual explanations of the model’s decision-making process, enhancing interpretability. The combination of multi-color space analysis, architectural optimizations, and explainable AI techniques positions PyroChroma as a significant contribution to the field of wildfire detection, with the potential for real-world deployment in early warning systems for wildfire risk management.</p>