Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion

Broccoli is a highly nutritious vegetable that is favored worldwide. Assessing and predicting the shelf life of broccoli holds considerable importance for effective resource optimization and management. The physicochemical parameters and spectral characteristics of broccoli are important indicators partially reflecting its shelf life. However, few studies have used spectral image information to predict and evaluate the shelf life of broccoli. In this study, multispectral imaging combined with multi-feature data fusion was used to predict and evaluate the shelf life of broccoli. Spectral data and textural features were extracted from multispectral images of broccoli and fused with the physicochemical parameters for analysis. Savitzky–Golay (SG) convolution smoothing and standard normal variate (SNV) and normalization (Norm) preprocessing methods were employed to preprocess the original spectral data and textural features, while a successive projection algorithm (SPA) was used to extract relevant feature bands. The physicochemical parameters for broccoli shelf life were predicted using three methods: support vector regression (SVR), random forest classification (RF), and 2D convolutional neural network (2D-CNN) models. Broccoli shelf life prediction models were evaluated using three classification methods: RF, 1D-CNN, and 2D-CNN. The results demonstrate that, among the models used for predicting and evaluating the shelf life of broccoli, the SPA+SG+RF classification model employing fused data Type C achieves the highest accuracy. Specifically, this method achieves accuracies of 88.98% and 88.64% for the training and validation sets, respectively. Multi-feature data fusion of spectral image information and physical and chemical parameters were combined with different machine learning methods to predict and evaluate the shelf life of broccoli.