Plant recognition represents a challenging computer vision problem due to the great variations of shape and texture among plant organs, within the same species. This paper proposes a lightweight , but reasonably deep Convolutional Neural Network architecture able to carry out this classification task. Multiple experiments were conducted with the proposed network architecture on the MEW2012 and Swedish leaf datasets. The experiments showed promising results, outperforming the current state-of-the-art systems that rely exclusively on a convolutional network for plant classification.