In this work, we explore various computer vision techniques, with a focus on texture recognition approaches, for the task of plant species detection. We particularly emphasize the study of a challenging dataset consisting of 50 Brazilian plant species' leaf midrib cross-sections using microscope images. The research focuses on a recent method named Random Encoding of Aggregated Deep Activation Maps (RADAM) that leverages deep features from pre-trained Convolutional Neural Networks (CNNs) for improved plant species identification. This method demonstrates significant advancement over traditional texture analysis and deep learning approaches, showcasing the potential of combining deep feature engineering with texture analysis for accurate plant species recognition.