Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (<i>Manihot esculenta</i> Crantz).

Plant Methods · 2020 · PMC7296968 · PMID 32549903

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highly significantno p-value reported

The results of canopy metrics (CCuav and CVuav) and VIs showed highly significant and positive correlation with LAI in all the tested phenological stages, whereas, CCuav and CVuav correlated with DMA with r value of 0.56 (Fig. 6 b).

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