PhenoDesk: A Digital Platform for Automated Leaf Phenotyping and Reproducible Morphometric Analysis
Year: 2026
Type: article
Abstract: PhenoDesk is a research-oriented digital phenotyping platform designed to facilitate automated leaf image analysis, morphological characterisation and reproducible plant phenotyping workflows. Developed primarily by Raquel Martínez-Peña during a competitively awarded José Castillejo international research fellowship at the Mountain Research Centre (CIMO), Polytechnic Institute of Bragança (Portugal), the platform combines computer vision, geometric analysis and interactive quality-control tools. Its current implementation includes automated leaf segmentation, individual object identification, geometric landmark estimation, morphometric measurements, manual scale calibration, visual quality control, sample selection and structured data export. The platform integrates a Python-based scientific analysis core with a web interface designed to support research workflows. The initial application focuses on almond leaves acquired under controlled imaging conditions. The software architecture is intended to facilitate future extensions to additional plant species and phenotyping applications. The platform is currently being used in research activities at CIMO, University of Burgos, and the Instituto Regional de Investigación y Desarrollo Agroalimentario y Forestal (IRIAF), demonstrating its initial adoption in institutional research environments beyond its original development context. The software remains under active development, and further methodological and experimental validation is ongoing. This record provides a technical overview of the platform, its current capabilities, research applications and development status. It does not constitute a public release of the source code or underlying unpublished experimental datasets. Software website: https://phenodesk.onrender.com/ Source code availability: Private repository; not publicly released at this stage.
Author: Martínez Peña, Raquel
Language: English
Subfield: Plant Science
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7224284132
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