Accurately predicting the estrus period is essential for enhancing the efficiency and lowering the costs of artificial insemination in livestock, a crucial sector for global food production. Precisely identifying the estrus period is critical to avoid economic losses such as decreased milk production, delayed calf births, and loss of eligibility for government subsidies. Since the most obvious movement that needs to be detected during the fertilization period is mounting, it is important to detect this movement. Since manual detection of this movement is difficult and costly, automated methods were needed. Therefore, it is thought that deep learning-based methods can be applied to detect the mounting moment. The proposed method detects the estrus period using deep learning and XAI (Explainable Artificial Intelligence) techniques. Deep learning-based mounting detection is performed using CNN, ResNet, VGG-19 and YOLO-v5 models. The ResNet model in this proposed study detects mounting movement with 99% accuracy. Explainability of deep learning models describes features that aid in decisionmaking in detecting mounting motion. Grad-CAM and Gradient Inputs models, which are XAI techniques, are used for the black box behind the proposed models. The developed deep learning models reveal that they focus on the udder and back area of the cows during the decision-making phase. In addition, how successfully the Grad-CAM and Gradient Inputs models, which are the XAI models used for the explainability of the deep learning models trained in this study, performed the explanation process was measured by calculating the "faithfulness", "maximum sensitivity" and "complexity" metrics