Geographical Origin Identification of Citrus Fruits Based on Near-Infrared Spectroscopy Combined with Convolutional Neural Network and Data Augmentation

Zhihong Lu, Kangkang Jia, Haoyang Zhang, Lei Tan, Saritporn Vittayapadung, Lie Deng and Qiang Lyu ()
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Zhihong Lu: Citrus Research Institute, Southwest University, Chongqing 400712, China
Kangkang Jia: Citrus Research Institute, Southwest University, Chongqing 400712, China
Haoyang Zhang: Citrus Research Institute, Southwest University, Chongqing 400712, China
Lei Tan: Citrus Research Institute, Southwest University, Chongqing 400712, China
Saritporn Vittayapadung: Faculty of Science and Technology, Chiangrai Rajabhat University, Chiang Rai 57100, Thailand
Lie Deng: Citrus Research Institute, Southwest University, Chongqing 400712, China
Qiang Lyu: Citrus Research Institute, Southwest University, Chongqing 400712, China

Agriculture, 2025, vol. 15, issue 22, 1-17

Abstract: Accurately determining citrus origin is essential for establishing and maintaining regional brands with distinctive qualities while safeguarding the rights and interests of both farmers and consumers. In this study, 2693 navel orange samples were collected from 13 major producing regions in China to establish a comprehensive near-infrared spectroscopy (NIRS) dataset. To address the challenge of citrus origin authentication, this study proposes a novel six-layer one-dimensional convolutional neural network (1D-CNN). The classification accuracy of this model reaches 96.16%. Compared with the support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), and three-layer 1D-CNNs with kernel sizes of 3 and 16, the accuracy of the proposed six-layer model is improved by 9.65%, 3.21%, 3.84%, and 1.98%, respectively. Furthermore, the dataset is augmented using a Wasserstein Generative Adversarial Network (WGAN) and Noise Addition. The results indicate that data augmentation can effectively improve the accuracy of various algorithm models. Among them, the 1D-CNN proposed in this study achieves the best performance on the Noise Addition-augmented dataset, with its accuracy, precision, recall, and F1-score reaching 98.39%, 0.9843, 0.9839, and 0.9840, respectively. Compared with the other four comparative models, the accuracy of this model is increased by 1.48%, 1.36%, 1.48%, and 2.85%, respectively. Finally, a visual analysis of the 1D-CNN’s feature-extraction process was conducted. The results demonstrate that the 1D-CNN can effectively extract discriminative NIR spectral features to accurately distinguish citrus from different origins and that data augmentation markedly improves model performance by increasing data diversity. This work provides a robust tool for citrus origin tracing and offers a new perspective for the origin authentication of other agricultural products.

Keywords: citrus; geographical origin identification; near-infrared spectroscopy; one-dimensional convolutional neural networks; data augmentation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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