Machine learning models based on hyperspectral imaging for pre-harvest tomato fruit quality monitoring

Traditional methods for assessing tomato quality are time-consuming, expensive, and limited in scope. Here we developed a non-destructive spectral-based model using a handheld hyperspectral camera with 204 bands at the 400–1000 nm range, focusing on data reduction, paving the way for an economically viable device designed to assess seven key tomato quality parameters. We collected 567 fruits from five cultivars of various types and analyzed them for weight, firmness, total soluble solids (TSS), citric acid, ascorbic acid, lycopene, and pH after taking hyperspectral images of the fruits. Five commonly used spectral indices, thousands of normalized difference spectral index (NDSI) combinations, a multivariable regression model (MVR), and three machine learning (ML) algorithms (random forest – RF, extreme gradient boosting – XGBoost, and artificial neural network – ANN) were employed to predict the quality parameters from as few bands as possible. Results show that the ML models with bands selected via a hotspot overlapping method significantly improved quality prediction compared to the common spectral index approaches. Among ML algorithms, RF stood out with the best results with R² of 0.94 for weight, 0.89 for firmness, 0.79 for lycopene, 0.72 for TSS, 0.67 for pH, 0.62 for citric acid, and 0.45 for ascorbic acid, with the only exception of ANN, which was slightly better for weight and lycopene (R² of 0.95 and 0.85, respectively). Overall, models with only five bands were enough to predict all seven quality parameters with comparable performance to models with a larger number of bands. Our study offers an efficient and cost-effective method for assessing pre-harvest tomato quality, benefiting farmers and the food industry, as well as scientific research on fruit development and nutrition.