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Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

文献类型： 外文期刊

作者： Zhao, Zikun 1 ; Xu, Sai 2 ; Lu, Huazhong 3 ; Liang, Xin 2 ; Feng, Hongli 1 ; Li, Wenjing 4 ;

作者机构： 1.South China Agr Univ, Coll Engn, Machan Engn, Guangzhou 510642, Peoples R China

2.Guangdong Acad Agr Sci, Inst Facil Agr, Guangzhou 510640, Peoples R China

3.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

4.Guangdong Acad Agr Sci, Inst Plant Protect, Guangzhou 510640, Peoples R China

关键词： visible/near-infrared spectroscopy; litchi stem borer; hyperspectral imaging; X-ray imaging; multi-sensor data fusion

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 11 期

页码：

收录情况： SCI

摘要： To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

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