Detection of Soluble Solid Content in Citrus Fruits Using Hyperspectral Imaging with Machine and Deep Learning: A Comparative Study of Two Citrus Cultivars

Yuxin Xiao, Yuanning Zhai, Lei Zhou, Yiming Yin, Hengnian Qi, Chu Zhang

Hyperspectral imaging (HSI) has broad applications for detecting the soluble solids content (SSC) of fruits. This study explores the integration of HSI with machine learning and deep learning to predict SSC in two mandarin varieties: Ponkan and Tianchao. Traditional machine learning models (support vector machines and partial least squares regression) and deep learning models (convolutional neural networks, long short-term memory, and Transformer architectures) were evaluated for SSC prediction performance. Combined models that integrated different deep learning architectures were also explored. Results revealed varietal differences in prediction performance. For Ponkan mandarins, the best SSC prediction model was achieved by partial least squares regression, outperforming deep learning models. In contrast, for Tianchao mandarins, the deep learning model based on convolutional neural network slightly surpassed the traditional model. SHapley Additive exPlanations (SHAP) analysis indicated that the influential wavelengths varied between varieties, suggesting differences in key spectral features for SSC prediction. These findings highlight the potential of combining HSI with advanced modeling for citrus SSC prediction, while emphasizing the need for variety-specific models. Future research should focus on developing more robust and generalized prediction models by incorporating a broader range of citrus varieties and exploring the impact of varietal characteristics on model performance.