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Fast and accurate image recognition algorithms for fresh produce food safety sensing

机译:快速准确的图像识别算法,用于生鲜食品安全感测

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This research developed and evaluated the multispectral algorithms derived from hyperspectral line-scan fluorescence imaging under violet LED excitation for detection of fecal contamination on Golden Delicious apples. The algorithms utilized the fluorescence intensities at four wavebands, 680 nm, 684 nm, 720 nm, and 780 nm, for computation of simple functions for effective detection of contamination spots created on the apple surfaces using four concentrations of aqueous fecal dilutions. The algorithms detected more than 99% of the fecal spots. The effective detection of feces showed that a simple multispectral fluorescence imaging algorithm based on violet LED excitation may be appropriate to detect fecal contamination on fast-speed apple processing lines.
机译:这项研究开发并评估了在紫光LED激发下从高光谱线扫描荧光成像得出的多光谱算法,用于检测Golden Delicious苹果的粪便污染。该算法利用680 nm,684 nm,720 nm和780 nm四个波段的荧光强度来计算简单函数,以有效检测使用四种浓度的粪便水溶液稀释液在苹果表面上产生的污染点。该算法检测到超过99%的粪便斑点。粪便的有效检测表明,基于紫罗兰色LED激发的简单多光谱荧光成像算法可能适用于检测快速苹果加工线上的粪便污染。

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