The process of studying plant cell traits has traditionally been conducted manually, requiring meticulous review to categorize and capture each separate cell trait of a plant. Our research aims to simplify and automate the categorization process by incorporating pre-trained deep learning-based models to detect and categorize plant cells in light micrographs obtained via an optical microscope. By manually labeling plant imaging data, we trained a Deep learning model based on YOLO model architecture to allow prediction on new plant stem micrographs. The model was trained to detect three categories of cells (vessels, fibers, and parenchyma) in the micrograph, achieving an overall precision of 0.74, a recall of 0.79, and an mAP50 of 0.77 in the test set. At the class level, vessels and fibers produced a high precision (mAP50 = 0.84 and 0.86, respectively), while parenchyma cells proved to be more challenging due to their smaller size and ambiguous boundaries, with mAP50 = 0.60 but a higher coverage of 0.80. These results highlight both the strengths and limitations of the approach, and suggest that future improvements in detecting subtle or rare cell types could further enhance performance. In summary, our results demonstrate the significant usefulness of automated cell detection and recognition in the field of hydraulic plant anatomy.