Inadvertent bird nest destruction by autonomous mowing machines poses significant threats to the breeding success of meadow birds. Drone-based detection methods represent the current state-of-the-art for bird nest localization to attain mower circumvention. However, they only identify 80% of bird nests with average localization error of 3.344 meters and are restricted to specific application times. This paper introduces alternative, fully automated nest localization systems integrated with autonomous mowers. Two strategies are proposed, 1) Directly detecting bird nests using thermal data, or 2) Indirectly, by tracking birds and extrapolating their trajectories back to their nests using RGBD data. These methods were validated with warmed chicken eggs hidden in grasslands and with drones simulating bird flight. YOLOv8 models were modified for both approaches. The thermal localization method is able to detect all bird nests with an average confidence of 73.4%. It allows for real-time localization and yields one unnecessary nest circumvention for every ten bird nests saved due to false positives. This method is shown to be effective in all breeding season temperatures, both day and night. Conversely, the trajectory extrapolation method detects birds with an average confidence of 82.2% and has localization error of 0.794 meters. Birds taking flight prematurely or from locations other than nests impact the number of bird nests saved and the number of unnecessary circumventions. It is demonstrated that this method fails to detect birds during nighttime. In conclusion, an automated thermal-based localization system integrated with autonomous mowers outperforms both RGBD- and current state-of-the-art drone-based methods. This study highlights therefore the potential of thermal-based solutions for bird nest protection in grasslands.