Classification of Sugar Content of Kiwi Fruit Based on Deep Learning and Near Infrared Spectrum

Abstract In recent years, the kiwifruit industry has undergone rapid development. The quality of kiwifruit significantly impacts its market price. The current manual grading system for kiwifruit is inefficient and lacks accuracy. Therefore, the application of intelligent methods for the rapid classification of kiwifruit has become a prominent topic in kiwifruit research. Sugar content is a critical factor in evaluating kiwifruit quality. This study establishes a non-destructive detection and classification model for sugar content using near-infrared spectroscopy and deep learning techniques. Initially, near-infrared spectra and sugar content values of kiwifruit were gathered. Following the classification of kiwifruit samples based on sugar content standards, two preprocessing methods – MSC and SNV – along with four deep learning network structures – LeNet, AlexNet, VGG, and ResNet – were utilized to construct classification models for the spectra and sugar content. Through comparison of training set accuracy, test set accuracy, and confusion matrix of the test set, the combination of MSC preprocessing and the ResNet classification model was identified as the optimal approach, achieving a test set accuracy of 100%. These findings indicate that the classification model established using near-infrared spectrum and deep learning effectively promotes kiwifruit classification based on sugar content.