Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time. (30th January 2022) - Yu, Dajun; Lord, Nick; Polk, Justin; Dhakal, Kshitiz; Li, Song; Yin, Yun; Duncan, Susan E.; Wang, Hengjian; Zhang, Bo; Huang, Haibo
Highlights: The physicochemical properties of edamame vary at different growth stages. Edamame harvested at the R6 growth stage has the best physicochemical properties. Machine learning methods based on the pods' spectra reflectance were developed. The machine learning methods can identify the optimal harvest time of edamame. Abstract: 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. The results showed that pod/bean weight and pod thickness peaked at R6 and remained stable thereafter. Sugar, starch, alanine, and glycine also peaked at R6 but proceeded to decline. The ML method (random forest classification) using pods' spectral reflectance had a high accuracy of 0.95 for classifying 'early' and 'late' samples and 0.87 for classifying 'early' and 'ready' samples. Therefore, this method can determine the optimal harvest time of edamame.