An unsupervised pipeline using ImageNet features, PCA/Isomap, and k-means is applied to 650 pollen images, but the claimed family-level identification is supported only by qualitative cluster inspection and non-specialist agreement.
Honey Authentication with Machine Learning Augmented Bright-Field Microscopy
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abstract
Honey has been collected and used by humankind as both a food and medicine for thousands of years. However, in the modern economy, honey has become subject to mislabelling and adulteration making it the third most faked food product in the world. The international scale of fraudulent honey has had both economic and environmental ramifications. In this paper, we propose a novel method of identifying fraudulent honey using machine learning augmented microscopy.
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cs.CV 1years
2019 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Unsupervised Representations of Pollen in Bright-Field Microscopy
An unsupervised pipeline using ImageNet features, PCA/Isomap, and k-means is applied to 650 pollen images, but the claimed family-level identification is supported only by qualitative cluster inspection and non-specialist agreement.