Core Ideas Extensive natural variation exists for tocochromanols in fresh sweet corn kernels. vte4 controls the levels of α‐tocopherol, which has the highest vitamin E activity. vte1 and hggt1 contribute to the genetic control of tocotrienols. Sweet corn lines possessing sh2 have the highest levels of ς‐ and γ‐tocotrienols. Prediction abilities were highest for tocotrienols relative to tocopherols. Sweet corn (Zea mays L.), a highly consumed fresh vegetable in the United States, varies for tocochromanol (tocopherol and tocotrienol) levels but makes only a limited contribution to daily intake of vitamin E and antioxidants. We performed a genome‐wide association study of six tocochromanol compounds and 14 derivative traits across a sweet corn inbred line association panel to identify genes associated with natural variation for tocochromanols and vitamin E in fresh kernels. Concordant with prior studies in mature maize kernels, an association was detected between γ‐tocopherol methyltransferase (vte4) and α‐tocopherol content, along with tocopherol cyclase (vte1) and homogentisate geranylgeranyltransferase (hggt1) for tocotrienol variation. Additionally, two kernel starch synthesis genes, shrunken2 (sh2) and sugary1 (su1), were associated with tocotrienols, with the strongest evidence for sh2, in combination with fixed, strong vte1 and hggt1 alleles, accounting for the greater amount of tocotrienols in su1sh2 and sh2 lines. In prediction models with genome‐wide markers, predictive abilities were higher for tocotrienols than tocopherols, and these models were superior to those that used marker sets targeting a priori genes involved in the biosynthesis and/or genetic control of tocochromanols. Through this quantitative genetic analysis, we have established a key step for increasing tocochromanols in fresh kernels of sweet corn for human health and nutrition.
Sweet corn (Zea mays L.) is highly consumed in the United States, but does not make major contributions to the daily intake of carotenoids (provitamin A carotenoids, lutein and zeaxanthin) that would help in the prevention of health complications.A genome-wide association study of seven kernel carotenoids and twelve derivative traits was conducted in a sweet corn inbred line association panel ranging from light to dark yellow in endosperm color to elucidate the genetic basis of carotenoid levels in fresh kernels. In agreement with earlier studies of maize kernels at maturity, we detected an association of β-carotene hydroxylase (crtRB1) with β-carotene concentration and lycopene epsilon cyclase (lcyE) with the ratio of flux between the αand β-carotene branches in the carotenoid biosynthetic pathway. Additionally, we found that 5% or less of the evaluated inbred lines possessing the shrunken2 (sh2) endosperm mutation had the most favorable lycE allele or crtRB1 haplotype for elevating β-branch carotenoids (β-carotene and zeaxanthin) or β-carotene, respectively. Genomic prediction models with genome-wide markers obtained moderately high predictive abilities for the carotenoid traits, especially lutein, and outperformed models with less markers that targeted candidate genes implicated in the synthesis, retention, and/or genetic control of kernel carotenoids. Taken together, our results constitute an important step toward increasing carotenoids in fresh sweet corn kernels.
The cuticle, a hydrophobic layer of cutin and waxes synthesized by plant epidermal cells, is the major barrier to water loss when stomata are closed at night and under water-limited conditions. Elucidating the genetic architecture of natural variation for leaf cuticular conductance (gc) is important for identifying genes relevant to improving crop productivity in drought-prone environments. To this end, we conducted a genome-wide association study of gc of adult leaves in a maize inbred association panel that was evaluated in four environments (Maricopa, AZ, and San Diego, CA, in 2016 and 2017). Five genomic regions significantly associated with gc were resolved to seven plausible candidate genes (ISTL1, two SEC14 homologs, cyclase-associated protein, a CER7 homolog, GDSL lipase, and β-D-XYLOSIDASE 4). These candidates are potentially involved in cuticle biosynthesis, trafficking and deposition of cuticle lipids, cutin polymerization, and cell wall modification. Laser microdissection RNA sequencing revealed that all these candidate genes, with the exception of the CER7 homolog, were expressed in the zone of the expanding adult maize leaf where cuticle maturation occurs. With direct application to genetic improvement, moderately high average predictive abilities were observed for whole-genome prediction of gc in locations (0.46 and 0.45) and across all environments (0.52). The findings of this study provide novel insights into the genetic control of gc and have the potential to help breeders more effectively develop drought-tolerant maize for target environments.
ObjectivesAutomated detection and quantification of plant diseases would enable more rapid gains in plant breeding and faster scouting of farmers’ fields. However, it is difficult for a simple algorithm to distinguish between the target disease and other sources of dead plant tissue in a typical field, especially given the many variations in lighting and orientation. Training a machine learning algorithm to accurately detect a given disease from images taken in the field requires a massive amount of human-generated training data.Data descriptionThis data set contains images of maize (Zea mays L.) leaves taken in three ways: by a hand-held camera, with a camera mounted on a boom, and with a camera mounted on a small unmanned aircraft system (sUAS, commonly known as a drone). Lesions of northern leaf blight (NLB), a common foliar disease of maize, were annotated in each image by one of two human experts. The three data sets together contain 18,222 images annotated with 105,705 NLB lesions, making this the largest publicly available image set annotated for a single plant disease.
Computer vision models that can recognize plant diseases in the field would be valuable tools for disease management and resistance breeding. Generating enough data to train these models is difficult, however, since only trained experts can accurately identify symptoms. In this study, we describe and implement a two-step method for generating a large amount of high-quality training data with minimal expert input. First, experts located symptoms of northern leaf blight (NLB) in field images taken by unmanned aerial vehicles (UAVs), annotating them quickly at low resolution. Second, non-experts were asked to draw polygons around the identified diseased areas, producing high-resolution ground truths that were automatically screened based on agreement between multiple workers. We then used these crowdsourced data to train a convolutional neural network (CNN), feeding the output into a conditional random field (CRF) to segment images into lesion and non-lesion regions with accuracy of 0.9979 and F1 score of 0.7153. The CNN trained on crowdsourced data showed greatly improved spatial resolution compared to one trained on expert-generated data, despite using only one fifth as many expert annotations. The final model was able to accurately delineate lesions down to the millimeter level from UAV-collected images, the finest scale of aerial plant disease detection achieved to date. The two-step approach to generating training data is a promising method to streamline deep learning approaches for plant disease detection, and for complex plant phenotyping tasks in general.
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