Deep Learning-Based Classification of Plant Xylem Tissue from Light Micrographs

Anatomical studies of plant hydraulic traits have traditionally been conducted by manual measurements of light micrographs. An automated process could expedite analysis and broaden the scope of questions that can be asked, but such an approach would require the ability to accurately classify plant cells according to their type. Our research evaluates a deep learning-based model which accepts a cropped cell image input alongside a broader cropped image which incorporates contextual information of that cell type's original cropped image, and learns to segregate these plant cells based off of the features of both inputs. Whilst a single cropped image classification yielded adequate results with outputs matching the ground-truth labels, we discovered that a second image input significantly bolstered the model's learning and accuracy (98.1%), indicating that local context provides important information needed to accurately classify cells. Finally our results imply a future application of our classifier to automatic cell-type detection in xylem tissue image cross sections.

Automated summary

Traditionally, anatomical studies of plant hydraulic traits involved manual measurements of light micrographs. Our research introduces a deep learning-based model that accurately classifies plant cells by utilizing both cropped cell images and broader cropped images that incorporate contextual information. The results demonstrate the importance of local context in accurately classifying cells, and suggest potential future applications in automatic cell-type detection in plant tissue images.