This paper describes the development of a system capable of capturing, processing, and analysing images of palm oil plantations in order to detect and identify bud rot disease. The process begins with the capturing of images using the DJI Phantom 4 UAV, which is first configured for a flight plan of the desired area and altitude. The resulting images are processed by photogrammetry software to create orthomosaics. The developed algorithm uses the grayscale of the generated orthomosaic and identifies the palms affected by bud rot. This is accomplished using the sliding-window method, by which smaller samples of the image are used and evaluated independently. Each sample is then extracted with a ULBP feature vector that numerically represents the texture of the image and is classified by means of a previously trained logistic regression model, allowing the recognition of positive cases of the disease. Possible positive cases are further distinguished using the non-maximum suppression algorithm. The system was tested with different images than the images used for training and for establishing the set point. As a result, the system showed a 92% precision and a 96% of sensitivity for bud rot disease detection. These results are satisfactory in terms of detecting bud rot disease using a low-cost system.