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首页> 外文会议>Sensing for agriculture and food quality and safety III >Hyperspectral near-infrared reflectance imaging for detection of defect tomatoes
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Hyperspectral near-infrared reflectance imaging for detection of defect tomatoes

机译：高光谱近红外反射成像检测缺陷番茄

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Cuticle cracks on tomatoes are potential sites of pathogenic infection that may cause deleterious consequences both to consumer health and to fresh and fresh-cut produce markets. The feasibility of hyperspectral near-infrared imaging technique in the spectral range of 1000 nm to 1700 nm was investigated for detecting defects on tomatoes. Spectral information obtained from the regions of interest on both defect areas and sound areas were analyzed to determine some an optimal waveband ratio that could be used for further image processing to discriminate defect areas from the sound tomato surfaces. Unsupervised multivariate analysis method, such as principal component analysis, was also explored to improve detection accuracy. Threshold values for the optimized features were determined using linear discriminant analysis. Results showed that tomatoes with defects could be differentiated from the sound ones, with an overall accuracy of 94.4%. The spectral wavebands and image processing algorithms determined in this study could be used for multispectral inspection of defects tomatoes
机译：西红柿上的角质层裂纹是潜在的病原体感染部位，可能对消费者健康以及新鲜和鲜切农产品市场造成有害影响。研究了在1000 nm至1700 nm光谱范围内使用高光谱近红外成像技术检测西红柿上的缺陷的可行性。分析从缺陷区域和声音区域上的感兴趣区域获得的光谱信息，以确定一些最佳波段比，该最佳波段比可用于进一步的图像处理，以将缺陷区域与声音番茄表面区分开。还探索了无监督多元分析方法，例如主成分分析，以提高检测精度。使用线性判别分析确定优化特征的阈值。结果表明，可以将有缺陷的西红柿与有缺陷的西红柿区分开，总体准确率为94.4％。本研究确定的光谱波段和图像处理算法可用于缺陷番茄的多光谱检查

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