Tehuacán-Cuicatlán Valley is a semi-arid zone in the south of Mexico. It was inscribed in the World Heritage List by the UNESCO in 2018. This unique area has wide biodiversity including several endemic plants. Unfortunately, human activity is constantly affecting the area. A way to preserve a protected area is to carry out autonomous surveillance of the area. A first step to reach this autonomy is to automatically detect and recognize elements in the area. In this work, we present a deep learning based approach for columnar cactus recognition, specifically, the Neobuxbaumia tetetzo species, endemic of the Valley. An image dataset was generated for this study by our research team, containing more than 10,000 image examples. The proposed approach uses this dataset to train a modified LeNet-5 Convolutional Neural Network. Experimental results have shown a high recognition accuracy, 0.95 for the validation set, validating the use of the approach for columnar cactus recognition.