Most object detection models deployed on unmanned aerial systems (UAS) focus on identifying objects in the visible spectrum, also known as Red-Green-Blue (RGB) imagery, due to availability and cost. Fusing long wave infrared (LWIR) images with RGB imagery can increase the resiliency and overall performance of a machine learning (ML) object detection model in changing environments. Because LWIR based ML models are not commonly utilized or studied there exists a gap in the literature that discusses performance metrics between LWIR, RGB and LWIR-RGB fused ML object detection models from various ground and air collection platforms. Understanding multispectral ML object detection performance from UAS is highly meaningful because of the increasing convergence of ML and UAS technologies. Therefore, the need to establish baseline performance metrics for how certain ML models perform on various ground and air platforms is necessary for effective implementation and deployment of these two technologies. Using object detection results from both ground and air platforms, this research provided a baseline RGB ML model mean average precision (mAP) of 95.1%, a LWIR ML model mAP of 94.5%, and a blended RGB-LWIR ML model mAP of 92.6%. This research contributes to the literature a labelled training dataset of 12,600 images for ground-based and air-based RGB, LWIR, and RGB-LWIR fused imagery, to encourage further multispectral machine-driven object detection research from both air and ground platforms.