Prediction of Sweetness and Nitrogen Content in Soybean Crops from High Resolution Hyperspectral Imagery

In this paper, we investigate a hyperspectral imagery data processing method to predict the sweetness and amino acid content of green vegetal soybean crops. Regression models based on neural networks were developed in order to calculate the level of sucrose, glucose, and nitrogen concentration, which can be related to sweetness and amino acid concentration of vegetables. We demonstrate the method using hyperspectral data of wavelengths ranging from the visible to the near infrared acquired from an experimental field of green vegetal soybeans. A performance analysis is reported comparing regression models built using datasets pre-processed using the first and second derivative analysis. The second derivative transformed dataset presented the best performance overall. Glucose could be predicted with greater accuracy.