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| Research on the Method of Online Detection of Hollow Watermelons Based on Full-Transmission Near-Infrared Spectroscopy |
| LI Jia-qi1, 2, 3, TIAN Xi2, 3, WANG Qing-yan2, 3, HE Xin2, 3, HUANG Wen-qian2, 3* |
1. Department of Vehicle Engineering, School of Mechanical Engineering, Guangxi University, Nanning 530004, China
2. Intelligent Equipment Research Center, Intelligent-Meaurment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3. National Research Center of Intelligent Equipment for Agriculture, Intelligent-Meaurment, Beijing 100097, China
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Abstract Watermelon has high nutritional value and is known for its effectiveness in relieving heat in medical applications. Key indicators for evaluating watermelon include ripeness, sweetness, and whether it is hollow. These factors significantly influence market competitiveness. Screening for hollow watermelons ensures higher quality, thereby enhancing market competitiveness. In this study, 307 watermelon spectra were collected using a fully transmissive near-infrared (NIR) spectroscopy device developed independently in our laboratory. Based on the characteristic that hollow areas in watermelons primarily occur at the center of the fruit, we innovatively propose segmenting and weighting the spectra. The optimal two weighted spectra were selected using Support Vector Machine (SVM) and Partial Least Squares Discriminant Analysis (PLSDA) algorithms. Classification models for hollow watermelons were then built using the original spectra and preprocessed with Multiplicative Scatter Correction (MSC) and Savitzky-Golay Smoothing (SGS) in combination with SVM and PLSDA. The results showed that preprocessing the spectra did not necessarily improve the model performance and could even decrease it compared to models built with the original spectra. The models established using the two weighted spectra achieved the best performance, with accuracies of 96.74% (SVM) and 92.39% (PLSDA). The weighted spectra provided better modeling performance than the original and other preprocessed spectra. The weighted spectra were selected using SVM and PLSDA algorithms, and the original spectra were used to establish classification models with one-dimensional convolutional neural networks (1D-CNN). The model accuracies were 98.92% (SVM), 96.77% (PLSDA), and 95.70% (original spectra). The results indicated that 1D-CNN provided better modeling performance than SVM and PLSDA. Additionally, the segmented and weighted spectra remained effective in 1D-CNN and performed better than the original spectra. This study provides important technical support for non-destructive online grading detection of watermelons.
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Received: 2024-06-03
Accepted: 2024-09-24
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Corresponding Authors:
HUANG Wen-qian
E-mail: huangwq@nercita.org.cn
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