Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time.

This study aims to investigate the changes in physical and chemical properties of edamame during bean development and apply a spectroscopy-based machine learning (ML) technique to determine optimal harvest time. The edamame harvested at R5 (beginning seed), R6 (full seed), and R7 (beginning maturity) growth stages were characterized for physical and chemical properties, and pods were measured for spectral reflectance (360-740 nm) using a handheld spectrophotometer. The samples were categorized into 'early', 'ready', and 'late' based on the characterized properties.