  Recently, Convolutional Neural Networks (CNNs) have made a great performance
for remote sensing image classification. Plant recognition using CNNs is one of
the active deep learning research topics due to its added-value in different
related fields, especially environmental conservation and natural areas
preservation. Automatic recognition of plants in protected areas helps in the
surveillance process of these zones and ensures the sustainability of their
ecosystems. In this work, we propose an Enhanced Randomly Initialized
Convolutional Neural Network (ERI-CNN) for the recognition of columnar cactus,
which is an endemic plant that exists in the Tehuac\'an-Cuicatl\'an Valley in
southeastern Mexico. We used a public dataset created by a group of researchers
that consists of more than 20000 remote sensing images. The experimental
results confirm the effectiveness of the proposed model compared to other
models reported in the literature like InceptionV3 and the modified LeNet-5
CNN. Our ERI-CNN provides 98% of accuracy, 97% of precision, 97% of recall,
97.5% as f1-score, and 0.056 loss.
