Order Rodentia is a massive group of animals. In case the population of which is not controlled, their sprawl and propagation will impose severe problems for humankind. One way to prevent and fight the growth of the Rodentia population is by poisoning their burrows, which requires finding the precise location of the holes made. In this study, a fast and intelligent algorithm for determining the position of rodent holes was presented. A quadcopter with a 1m.s-1 propeller speed at a constant 1m height from the ground was used for aerial filming. The filming rate was 25 frames per second. Movies prepared by the camera were transmitted to a computer via wireless communications and converted into images by Aoao software. From images, red, green, blue, and gray surfaces and co-occurrence matrices were extracted, from which, in turn, 88 color and 44 texture features were extracted. Once top features, most of which related to the texture of images, were selected, they were used to train and implement a support vector machine (SVM) classifier with the radial base kernel. The maximum classification accuracy was obtained with a Kernel breadth of 0.1. This method 
detected the rodent nest hole with very high accuracy (100%). The minimum and maximum differences between the exact and predicted positions were 1 and 24 cm.