Research on Non-destructive Detection of Kiwi Fruit Internal Quality Based on Deep Learning

Abstract

 In recent years, as people's awareness of dietary health has continuously increased, the nutritional value and intrinsic quality of fruits have gradually become the focus of consumers' attention. However, in China, the classification of fresh fruits still relies on empirical methods such as manual observation, lacking unified and scientific standards, resulting in uneven fruit quality in the market. To improve the accuracy and efficiency of fruit quality classification, this paper takes kiwifruit as the research object and proposes a non-destructive detection technology that integrates near-infrared spectroscopy technology and deep learning models to achieve kiwifruit quality classification based on soluble solid content (SSC). This study first collected full-band spectral data of kiwifruit samples using a near-infrared spectrometer and combined the physicochemical values of SSC measured by a refractometer to establish a complete dataset containing spectral and quality labels. In the data preprocessing stage, three common algorithms, MSC, SG, and CT, were tried, and their effects in noise reduction and smoothing were compared. The experiment found that the SG preprocessing method could most effectively improve the overall performance of the model. Subsequently, this paper built a deep neural network model based on the LSTM model, combining BiLSTM (bidirectional long short-term memory network) and MHA (multi-head attention mechanism), to better capture the temporal features and key band information in the spectral sequence. The final results show that the combination of SNV + BiLSTM + MHA performs excellently in the classification detection of apple SSC content, with an accuracy rate of 0.9574, a precision rate of 0.9575, a recall rate of 0.9601, and an F1 score of 0.9586, fully verifying the effectiveness and application potential of this method in the non-destructive detection of kiwifruit quality.