The genome of Arabidopsis (Arabidopsis thaliana) contains five sequences with high similarity to FLAVONOL SYNTHASE1 (AtFLS1), a previously characterized flavonol synthase gene that plays a central role in flavonoid metabolism. This apparent redundancy suggests the possibility that Arabidopsis uses multiple isoforms of FLS with different substrate specificities to mediate the production of the flavonols, quercetin and kaempferol, in a tissue-specific and inducible manner. However, biochemical and genetic analysis of the six AtFLS sequences indicates that, although several of the members are expressed, only AtFLS1 encodes a catalytically competent protein. AtFLS1 also appears to be the only member of this group that influences flavonoid levels and the root gravitropic response in seedlings under nonstressed conditions. This study showed that the other expressed AtFLS sequences have tissue- and cell type-specific promoter activities that overlap with those of AtFLS1 and encode proteins that interact with other flavonoid enzymes in yeast two-hybrid assays. Thus, it is possible that these “pseudogenes” have alternative, noncatalytic functions that have not yet been uncovered.

In soybean (Glycine max (L.) Merr.), stems remain a competitive carbon resource sink during reproduction. Radial stem expansion after termination of axial stem growth requires resources to produce secondary cell walls in secondary vascular tissues. The impact of both the timing and extent of secondary growth in stems on fruit production in soybean is unknown. We hypothesized that cultivars gaining higher amounts of secondary vascular tissues during seed filling would experience lower reproductive output. Four determinate cultivars were grown in a greenhouse and harvested at the beginning and ending of seed filling for biomass determination and tissue composition measurement (ImageJ-Fiji). Cultivar ‘Hanover’ entered seed filling with the lowest amount of secondary phloem. It produced the largest increase in radial stem expansion, largest seeds, and fewest undeveloped (aborted) fruits. The physiological role of active secondary phloem production in stems for seed production in soybean reproduction is unclear. We reject our initial hypothesis and propose that high fitness may require the concomitant growth of vegetative and reproductive organs.

Resources for soybean [Glycine max (L.) Merr] pods are supplied initially from newly assimilated chemicals. As reproduction progresses, dependence on stored reserves increases. Seed weight is sensitive to stored shoot reserves, but the role of stem resources in late seed filling is unclear. Pith and cortex tissues contain living cells in stems and play a key role in storage. We hypothesized that main stem tissue composition can predict concentrations of mobilizable resources, which can be used to calculate impacts of mobilizable chemicals on yield. A field experiment with determinate cultivars investigated stem tissue composition, mobile and immobile chemicals, and their relationship with fruit production. Pith was correlated with non-structural chemicals; xylem and phloem predicted structural chemicals. During late seed filling, mobile stem chemicals decreased 67.5%, and we found a negative relationship between pith and aborted pods. Results support our hypotheses and highlight the importance of pith in remobilized resources for seed production in soybean.

A challenge for precisin agriculture is developing automated computer methods to accurately estimate fruit and seed yield in the standing crop. Soybean ( Glycine max (L.) Merr.) pods are hard to distinguish from stems, which causes inaccurate predictions of yield from images of mature shoots. We developed image analysis tools to estimate morphological traits in the vertical canopy profile that are associated with high seed yield in soybeans. Using common image processing methods involving thresholding and particle analysis, higher circularity of the shoot convex hull vertical profile was found to correlate with high seed yield (number and grams per plant) in both an indeterminate cultivar (P49T80R) and in a determinate cultivar (Glenn). These soybean cultivars achieved high yields using different growth and production strategies. Glenn had a smaller shoot but exhibited a high pod density phenotype throughout its canopy (PT1, where PT stands for phenotype), while P49T80R achieved high yield through a combination of increased height and greater branching width, which compensated for lower pod density in its branches (PT2). We trained a deep machine learning model to automate shoot phenotyping using nearly 400 images of soybean shoots. The resulting model distinguished between PT1 and PT2 shoot images with 80% overall accuracy. The highest prediction accuracy in the model, 95%, was attained for shoots exhibiting the PT2 phenotype. Our work illustrates real‐world application of image analysis technologies to identify high‐yield trait analysis in field‐grown soybeans and emphasizes the importance of including pod density positioning in machine learning training models.