Determining the genetic control of root system architecture (RSA) in plants via large-scale genome-wide association study (GWAS) requires high-throughput pipelines for root phenotyping. We developed Core Root Excavation using Compressed-air (CREAMD), a high-throughput pipeline for the cleaning of field-grown roots, and Core Root Feature Extraction (COFE), a semiautomated pipeline for the extraction of RSA traits from images. CREAMD-COFE was applied to diversity panels of maize (Zea mays) and sorghum (Sorghum bicolor), which consisted of 369 and 294 genotypes, respectively. Six RSA-traits were extracted from images collected from .3,300 maize roots and .1,470 sorghum roots. Single nucleotide polymorphism (SNP)-based GWAS identified 87 TAS (trait-associated SNPs) in maize, representing 77 genes and 115 TAS in sorghum. An additional 62 RSA-associated maize genes were identified via expression read depth GWAS. Among the 139 maize RSA-associated genes (or their homologs), 22 (16%) are known to affect RSA in maize or other species. In addition, 26 RSA-associated genes are coregulated with genes previously shown to affect RSA and 51 (37% of RSA-associated genes) are themselves transe-quantitative trait locus for another RSA-associated gene. Finally, the finding that RSA-associated genes from maize and sorghum included seven pairs of syntenic genes demonstrates the conservation of regulation of morphology across taxa.

This perspective lays out a framework to enable the breeding of crops that can meet worldwide demand under the challenges of global climate change. Past work in various fields has produced multiple prediction methods to contribute to different plant breeding objectives. Our proposed framework focuses on the integration of these methods into decision-support tools that quantify the effects on multiple objectives of decisions made throughout the plant breeding pipeline. We discuss the complementarities among these methods with an emphasis on integration into tools that utilize operations research and systems approaches to help plant breeders rapidly and optimally design new cultivars under extant time, cost, and environmental constraints. In illustrating this potential, we demonstrate the interconnectedness and probabilistic nature of plant breeding objectives and highlight research opportunities to refine and combine knowledge across multiple disciplines. Such a framework can help plant breeders more efficiently breed for future environments, including so-called minor crops, leading to an overall increase in the resiliency of global food production systems. ll

Adverse environmental conditions such as heat and salt stress create endoplasmic reticulum (ER) stress in maize and set off the unfolded protein response (UPR). A key feature of the UPR is the upregulation of ZmbZIP60 and the splicing of its messenger RNA. We conducted an association analysis of a recombinant inbred line (RIL) derived from a cross of a tropical founder line, CML52 with a standard temperate line, B73. We found a major QTL conditioning heat-induced ZmbZIP60 expression located cis to the gene. Based on the premise that the QTL might be associated with the ZmbZIP60 promoter, we evaluated various maize inbred lines for their ability to upregulate the expression of ZmbZIP60 in response to heat stress. In general, tropical lines with promoter regions similar to CML52 were more robust in upregulating ZmbZIP60 in response to heat stress. This finding was confirmed by comparing the strength of the B73 and CML52 ZmbZIP60 promoters in transient maize protoplast assays. We concluded that the upstream region of ZmbZIP60 is important in conditioning the response to heat stress and was under selection in maize when adapted to different environments.Summary: Heat stress has large negative effects on maize grain yield. Heat stress creates ER stress in maize and sets off the UPR. We searched for factors conditioning heat induction of the UPR in maize seedlings by conducting an association analysis based on the upregulation of unspliced and spliced forms of ZmbZIP60 mRNA (ZmbZIP60u and ZmbZIP60s, respectively). ZmbZIP60u was upregulated more robustly by heat stress in the tropical maize line, CML52, than in B73, and a major QTL derived from the analysis of RILs from a cross of these two lines mapped in the vicinity of ZmbZIP60. We conducted a cis/trans test to determine whether the QTL was acting as a cis regulatory element or in trans, as might be expected for a transcription factor. We found that the QTL was acting in cis, likely involving the ZmbZIP60 promoter. ZmbZIP60 promoters in other temperate and tropical lines similar to CML52 showed enhanced expression of ZmbZIP60u by heat. The contribution of the CML52 promoter to heat induction of ZmbZIP60 was confirmed by analyzing the CML52 and B73 promoters linked to a luciferase reporter and assayed in heat-treated maize protoplasts.

High-throughput phenotyping enables the efficient collection of plant trait data at scale. One example involves using imaging systems over key phases of a crop growing season. Although the resulting images provide rich data for statistical analyses of plant phenotypes, image processing for trait extraction is required as a prerequisite. Current methods for trait extraction are mainly based on supervised learning with human labeled data or semisupervised learning with a mixture of human labeled data and unsupervised data. Unfortunately, preparing a sufficiently large training data is both time and labor-intensive. We describe a self-supervised pipeline (KAT4IA) that uses K-means clustering on greenhouse images to construct training data for extracting and analyzing plant traits from an image-based field phenotyping system. The KAT4IA pipeline includes these main steps: self-supervised training set construction, plant segmentation from images of field-grown plants, automatic separation of target plants, calculation of plant traits, and functional curve fitting of the extracted traits. To deal with the challenge of separating target plants from noisy backgrounds in field images, we describe a novel approach using row-cuts and column-cuts on images segmented by transform domain neural network learning, which utilizes plant pixels identified from greenhouse images to train a segmentation model for field images. This approach is efficient and does not require human intervention. Our results show that KAT4IA is able to accurately extract plant pixels and estimate plant heights.

High-throughput phenotyping is a modern technology to measure plant traits efficiently and in large scale by imaging systems over the whole growth season. Those images provide rich data for statistical analysis of plant phenotypes. We propose a pipeline to extract and analyze the plant traits for field phenotyping systems. The proposed pipeline include the following main steps: plant segmentation from field images, automatic calculation of plant traits from the segmented images, and functional curve fitting for the extracted traits. To deal with the challenging problem of plant segmentation for field images, we propose a novel approach on image pixel classification by transform domain neural network models, which utilizes plant pixels from greenhouse images to train a segmentation model for field images. Our results show the proposed procedure is able to accurately extract plant heights and is more stable than results from Amazon Turks, who manually measure plant heights from original images.