Prediction of Sugar Level in Grapes Using Multispectral Imaging

Tracking sugar intake has become a trending practice, much like keeping track of calorie intake. However, consumer-grade systems or devices for measuring or detecting sugar levels of unlabeled food items are close to none. In this paper, we discuss how low-cost Multi-Spectral Imaging (MSI) technology can be used to identify the sugar level of grapes in a simple, yet effective manner. Here, grapes are used as a representative for fruit or food. Freshly harvested grapes were used for spectral data collection. Unispectral portable multispectral camera EVK – UNS52000 which has a spectral range of 700-940 nm across 10 spectral bands was used for data collection. Actual sugar content was determined using a handheld refractometer and used for reference. We evaluated the performance of two well-known machine learning-based regression algorithms such as Random Forest (RF) and Light Gradient Boosting Machines (LightGBM). Spectral data derived from images captured in 10 bands were used as features or independent variables and actual sugar measured using a refractometer was used as a reference or the dependent variable in regression models. We tested the model performance using different feature scenarios: a) All 10 spectral bands, b) Top 5 spectral bands and c) Top 3 spectral bands. Results showed that R2 was 0.92 for both RF and LightGBM with negligible variation in RMSE (1.05-1.06 °Brix) when using all 10 bands. There was minimal difference in terms of R2 and RMSE. However, there was a significant difference in model training time with LightGBM being much faster than RF. Moreover, by selecting the top 5 and 3 features, we reduced the R2 to 0.89 and 0.78 respectively in the case of LightGBM. While there is a reduction in R2, selecting the top 5 bands will be useful from an operational perspective in terms of computational power and cost of device development. We aim to design and develop the device using the selected top 5 bands for operational on-the-fly prediction of sugar content in grapes. The proposed approach could be helpful for various stakeholders such as people with Hypoglycemia or Diabetes, grape producers to gauge the maturity of grapes, and industrial use cases for vineyards.