The objective of this work was to use deep learning and images taken by unmanned aerial vehicles to develop a model to identify the occurrence of maca & uacute;ba (Acrocomia intumescens) palm trees. The model was trained and tested using data from areas in the southern region of the state of Cear & aacute;, Brazil. Later, the tested model was evaluated using data from areas in the Midwestern region of the country. The primary challenge was to distinguish maca & uacute;ba from other native palm trees, such as babassu (Attalea speciosa). Babassu has spectral similarities and a random distribution, which makes it difficult to identify. Red-green-blue mosaics were cropped into smaller size images and processed using a convolutional neural network deep-learning technique. Identification performance was evaluated using metrics of accuracy, precision, recall, and F1-score. In an area of 1,000 ha, 3,679 maca & uacute;ba palm trees and 12,410 babassu palm trees were identified, achieving a 93% accuracy. The proposed approach was evaluated in a 4.0 ha site located in the municipality of Bataypor & atilde;, in the southern region of the state of Mato Grosso do Sul, with an 89% accuracy. The model was able to identify maca & uacute;ba palm trees occurring in natural areas in the Semiarid and in Midwestern regions of Brazil.