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Development of a multi-spectral imaging system for the detection of bruises on apples

机译：开发用于检测苹果瘀伤的多光谱成像系统

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Early detection of bruises on apples is important for an automatic apple sorting system. A hyperspectral imaging system with the wavelength range of 400 to l000nm was built for detecting bruises happened in an hour on 'Fuji' apples. Principal components analysis (PCA) was conducted on the hyperspecrtral images and the principal components (PC) images were compared. Three effective wavelengths 780, 850 and 960nm were determined using the weighing coefficients plot of the best PC image. Then, a multi-spectral imaging system with three bands 780, 850 and 960nm in the near-infrared range was developed. The system was consisted of two beamsplitters at 805 and 900nm, two bandpass filters and halogen tungsten lamp, and three CCD cameras. Images of 20 intact and 20 bruised apples were acquired. PCA was conducted on the three-band images of each apple and the best PC image was selected for bruise detection. A bruise detection algorithm based on the PC images and a global threshold method was developed. Results show that 90% of the bruised apples are correctly recognized.
机译：早期发现苹果上的瘀伤对于自动苹果分选系统很重要。建立了一个波长范围为400至1000nm的高光谱成像系统，用于检测在一个小时内在“富士”苹果上发生的瘀伤。对高光谱图像进行主成分分析（PCA），并比较主成分（PC）图像。使用最佳PC图像的权重系数图确定了三个有效波长780、850和960nm。然后，开发了在近红外范围内具有三个波段780、850和960nm的多光谱成像系统。该系统由两个在805和900nm处的分束器，两个带通滤光片和卤素钨灯以及三个CCD相机组成。采集了20个完整的苹果和20个瘀伤的苹果的图像。对每个苹果的三波段图像进行PCA，并选择最佳PC图像进行瘀伤检测。提出了一种基于PC图像的损伤检测算法和全局阈值方法。结果表明，正确识别了90％的青苹果。

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