Abstract: To achieve rapid and non-destructive identification of early bruises in Actinidia arguta, this study proposed a parallel modeling strategy based on spectra and Gramian angular field (GAF) images. Hyperspectral data were collected at different times during the early stage of bruising, and the raw spectra were denoised using adaptive wavelet transform (AWT). Based on the denoised one-dimensional spectral data, four classification models were developed to classify the occurrence time of bruising in A. arguta fruits: support vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), long short-term memory network (LSTM), and stacking ensemble learning. Meanwhile, the study innovatively introduced the GAF method to transform the spectra into two-dimensional images, which were used in combination with the YOLOv8n model for the classification of bruising time. The results demonstrated that the models differed in classification performance and computational efficiency. Among them, the stacking model, which leverages the complementary strengths of multiple base learners, achieved the best classification performance, with accuracy, recall, precision and F1-score all reaching over 97.78%, and a running time of 49 seconds. The YOLOv8n model exhibited comparable accuracy, recall, precision, and F1-score to the stacking model, and although it incurred a higher computational cost (running time of 25 min and 22 s), its end-to-end image modeling capability and superior feature visualization significantly enhanced the interpretability of the model. The dual-path modeling strategy proposed in this study provides a new approach for the rapid and accurate detection of early bruises in A. arguta fruits, offering theoretical support and guidance for the development of intelligent postharvest grading and automated sorting systems.

Key words: Actinidia arguta; bruise; hyperspectral imaging; Gramian angular field; deep learning