Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (<i>Lactuca sativa</i> L.).

The results showed that the morphological features exhibited significant nonlinear interactions, such as highly significant positive correlations ( P < 0.01) between area and length and width in the binary features, and highly significant positive correlations (P < 0.01) between RatioWidthLength and Ellipse Compactness In addition, highly significant positive correlations (P < 0.01) were observed between CIELab L* and CIELab A* and Saturation (P < 0.01), indicating a highly significant negative correlation in the color features.

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