Copilot as a methodological partner in plant physiology: Proof of concept using common bean leaf dehydration

Quintana-Zaez J.C.; Ynchausti J.; Figueroa M.D.L.T.Y.; Chico-Ruíz J.; Acosta Y.; Vicente O.; Lorenzo J.C. · Eurobiotech Journal (2026) · DOI: 10.2478/ebtj-2026-0016

Resumen

Artificial intelligence (AI) is transforming plant science by enabling rapid data analysis, predictive modeling, and precision breeding. Image recognition accelerates phenotyping, while machine learning optimizes bioprocesses, improving both reproducibility and scalability. Deep Learning technologies, and cloud services expand accessibility, with Copilot exemplifying AI’s role by documenting leaf dehydration in Phaseolus vulgaris L. under controlled conditions. Statistical analysis confirmed a significant weight reduction from 0.25 g to 0.13 g (52%). Copilot generated structured annotations of morphological changes, including loss of turgor, increased venation prominence, and surface wrinkling. Mathematical overlays revealed fractal branching and Voronoi tessellation patterns, with dehydration exaggerating vein relief and sharpening boundaries, linking morphological traits to physiological stress responses. Additionally, Copilot supported bilingual figure legends, terminology harmonization, and bibliometric keyword suggestions, streamlining reproducibility and clarity. Overall, this AI-human synergy demonstrates Copilot’s value as a methodological partner, enhancing accessibility, precision, and international impact in plant physiology research.

Instituciones: Universidad Nacional de Trujillo; Universidad Nacional Agraria La Molina