Fast and accurate object detection is a desire of many vision-guided robotics based systems. Agriculture is an area where detection accuracy is often sacrificed for speed, especially in the pursuit of real time results. Pastoral landscapes are especially challenging with varying levels of complexity, as competing objects are rarely textually smooth or visibly different from surroundings. This study presents a machine learning algorithm designed for object detection called the Multiple Expert Colour Extreme Learning Machine (MEC-ELM). The MEC-ELM is a multiple expert implementation of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM is itself a modification of the Extreme Learning Machine (ELM) with a partially connected hidden layer and a fully connected output layer, taking 3 inputs. The inputs can be utilised by multiple colour systems, including, RGB, Y'UV and HSV. Colour inputs were chosen, as colour is not sensitive to adjustments in scale, size and location and provides information not available in the standard grey-scale ELM. In the MEC-ELM algorithm, feature extraction and classification techniques were implemented simultaneously making a fully functional object detection algorithm. The algorithm was tested on weed detection and cattle detection from a video feed, delivering 0.89 (cattle) to 0.98 (weeds) accuracy in tuning and a precision of 0.61 to 0.95 in testing, with classification times between 0.5s to 1s per frame. The algorithm has been designed with complex and unpredictable terrain in mind, making it an ideal application for agricultural or pastoral landscapes
