Within the scope of this project, a spectroscopy-dependent machine learning (ML) method will be utilized to estimate the optimal harvest time for mung bean, which will be used to examine the changes in physical and chemical attributes of the bean as it develops. It was decided to harvest mung bean from the R5 (initial seed), R6 (full seed), and R7 (beginning maturity) stages. The spectral reflectance of the pods was measured, and their physical and chemical characteristics were characterized.

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... e experiment was carried out using a spectrophotometer with a wavelength range of 360–740 nm. On the basis of the qualities that have been identified so far in the study, early, ready, and late specimens have all been included. The results showed that the pod/bean weight and pod thickness reached their maximum at R6. After that, everything remained the same as before. Around R6, there was an increase in sugar, carbs, amino acids, and glycine, among other things. The ML approach (random forest classification) achieved an accuracy of 0.95 for the classification of pods dependent on their spectral reflectance. Specimens can be classed as "early" or "late" depending on whether or not they are "ready" or "not ready" when they are collected or processed. As a result, this procedure is the most effective choice available. It can figure out when the best time is to harvest mung bean.