A method of monitoring produce freshness with hyperspectral imaging and machine learning is described as a way to reduce food waste in grocery stores. The method relies on hyperspectral reflectance images in the visible–near-infrared spectral range from 387.12 to 1023.5 nm with a 2.12-nm spectral resolution. The images were recorded in a laboratory with the imager viewing produce samples illuminated by broadband halogen lights, but we also recorded and discussed the implications of the illumination spectrum of lights found in a variety of grocery stores. A convolutional neural network was used to perform freshness classification for potatoes, bananas, and green peppers. Additionally, a genetic algorithm (GA) was used to determine the wavelengths carrying the most useful information for age classification, with an eye toward a future multispectral imager. Hyperspectral images were processed to explore the use of RGB images, GA-selected multispectral images, and full-spectrum hyperspectral images. The GA-based feature selection method outperformed RGB images for all tested produce, outperformed hyperspectral imagery for bananas, and matched hyperspectral imagery performance for green peppers. This feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores.

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使用高光谱成像和机器学习评估农产品新鲜度

一种通过高光谱成像和机器学习来监测农产品新鲜度的方法被描述为一种减少杂货店食物浪费的方法。该方法依赖于 387.12 至 1023.5 nm 的可见-近红外光谱范围内的高光谱反射图像，光谱分辨率为 2.12 nm。这些图像是在实验室中记录的，成像仪查看产品样本，由宽带卤素灯照亮，但我们也记录并讨论了在各种杂货店中发现的照明光谱的影响。使用卷积神经网络对土豆、香蕉和青椒进行新鲜度分类。此外，遗传算法 (GA) 用于确定携带最有用的年龄分类信息的波长，着眼于未来的多光谱成像仪。处理高光谱图像以探索 RGB 图像、GA 选择的多光谱图像和全光谱高光谱图像的使用。基于 GA 的特征选择方法在所有测试产品上的表现优于 RGB 图像，香蕉的高光谱图像表现优于青椒的高光谱图像性能。这种特征选择方法被用于开发一种低成本的多光谱成像仪，用于监控杂货店的产品。并匹配青椒的高光谱图像性能。这种特征选择方法被用于开发一种低成本的多光谱成像仪，用于监控杂货店的产品。并匹配青椒的高光谱图像性能。这种特征选择方法被用于开发一种低成本的多光谱成像仪，用于监控杂货店的产品。