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Abstract The rich diversity of angiosperms, both the planet’s dominant flora and the cornerstone of agriculture, is integrally intertwined with a distinctive evolutionary history. Here, we explore the interplay between angiosperm genome organization and botanical diversity, empowered by genomic approaches ranging from genetic mapping to analysis of gene regulation. Commonality in the genetic hardware of plants has enabled robust comparative genomics that has provided a broad picture of angiosperm evolution and implicated both general processes and specific elements in contributing to botanical diversity. We argue that the hardware of plant genomes – both in content and in dynamics – has been shaped by selection for rather substantial differences in gene regulation between plants and animals such as maize and human, organisms of comparable genome size and gene number. Their distinctive genome content and dynamics may reflect in part the indeterminate development of plants that puts strikingly different demands on gene regulation than in animals. Repeated polyploidization of plant genomes and multiplication of individual genes together with extensive rearrangement and differential retention, provides rich raw material for selection of morphological and/or physiological variations conferring fitness in specific niches, whether natural or artificial. These findings exemplify the burgeoning information available to employ in increasing knowledge of plant biology, and in modifying selected plants to better meet human needs. 

Societal Impact StatementPlant breeding is a critical tool for increasing the productivity, climate resilience, and sustainability of agriculture, but current phenotyping methods are a bottleneck due to the amount of human labor involved. Here, we demonstrate high‐throughput phenotyping with an unmanned aerial vehicle (UAV) to analyze the season‐long flowering pattern in cotton, subsequently mapping relevant genetic factors underpinning the trait. Season‐long flowering is a complex trait, with implications for adaptation of perennials to specific environments. We believe our approach can improve the speed and efficacy of breeding for a variety of woody perennials. SummaryMany perennial plants make important contributions to agroeconomies and agroecosystems but have complex architecture and/or long flowering duration that hinders measurement and selection. Iteratively tracking productivity over a long flowering/fruiting season may permit the identification of genetic factors conferring different reproductive strategies that might be successful in different environments, ranging from rapid early maturation that avoids stresses, to late maturation that utilizes the full seasonal duration to maximize productivity.In cotton, a perennial plant that is generally cultivated as an annual crop, we apply aerial imagery and deep learning methods to novel and stable genetic stocks, identifying genetic factors influencing the duration and rate of fruiting.Our phenotyping method was able to identify 24 QTLs that affect flowering behavior in cotton. A total of five of these corresponded to previously identified QTLs from other studies.While these factors may have different relationships with crop productivity and quality in different environments, their determination adds potentially important information to breeding decisions. With transfer learning of the deep learning models, this approach could be applied widely, potentially improving gains from selection in diverse perennial shrubs and trees essential to sustainable agricultural intensification. 

Abstract Yield improvement in cotton could be accelerated through selection for functional yield drivers such as interception of cumulative photosynthetically active radiation (∑IPAR), radiation use efficiency (RUE), and harvest index (HI). However, information on the extent to which these traits vary in cotton in the southeastern United States is limited. It was hypothesized that functional yield drivers would vary significantly within a diverse cotton collection. This study was conducted in Tifton and Athens, GA, and included a total of 4 site‐years. Lint yield, total biomass production, ∑IPAR, RUE, and HI were all affected by genotype. Biomass was more strongly correlated with RUE than ∑IPAR. Even among the highest yielding genotypes, values for functional yield drivers (biomass and harvest index) differed significantly, indicating that high yields could be achieved by differentially manipulating these underlying traits. However, when considered for all genotypes, only HI exhibited a significant positive correlation with yield. Boll production and intra‐boll yield components were also affected by genotype. When considered across upland genotypes, lint per boll, lint per seed, and lint percent were strongly associated with HI and lint yield, whereas boll mass and seed number per boll were not. We conclude that the genotypes evaluated in the current study achieve high lint production per boll and lint yields by manipulating different yield drivers. However, lint yield was primarily maximized through an increase in HI due to increases in boll production and within‐boll distribution of biomass to fiber, not due to increases in total biomass production or boll size. 

Estimation of cotton yield before harvest offers many benefits to breeding programs, researchers and producers. Remote sensing enables efficient and consistent estimation of cotton yields, as opposed to traditional field measurements and surveys. The overall goal of this study was to develop a data processing pipeline to perform fast and accurate pre-harvest yield predictions of cotton breeding fields from aerial imagery using machine learning techniques. By using only a single plot image extracted from an orthomosaic map, a Support Vector Machine (SVM) classifier with four selected features was trained to identify the cotton pixels present in each plot image. The SVM classifier achieved an accuracy of 89%, a precision of 86%, a recall of 75%, and an F1-score of 80% at recognizing cotton pixels. After performing morphological image processing operations and applying a connected components algorithm, the classified cotton pixels were clustered to predict the number of cotton bolls at the plot level. Our model fitted the ground truth counts with anR²value of 0.93, a normalized root mean squared error of 0.07, and a mean absolute percentage error of 13.7%. This study demonstrates that aerial imagery with machine learning techniques can be a reliable, efficient, and effective tool for pre-harvest cotton yield prediction. 

Ethyl methanesulfonate (EMS) mutagenesis offers important advantages for improving crops, such as cotton, with limited diversity in elite gene pools. EMS-induced point mutations are less frequently associated with deleterious traits than alleles from wild or exotic germplasm. From 157 mutant lines that have significantly improved fiber properties, we focused on nine mutant lines here. A total of eight populations were developed by crossing mutant lines in different combinations into GA230 (GA2004230) background. Multiple lines in each population were significantly improved for the fiber trait that distinguished the donor parent(s), demonstrating that an elite breeding line (GA230) could be improved for fiber qualities using the mutant lines. Genotypes improved for multiple fiber traits of interest suggesting that allele pyramiding is possible. Compared to midparent values, individual progeny in the population conferred fiber quality improvements of as much as 31.7% (in population O) for micronaire (MIC), 16.1% (in population P) for length, 22.4% (in population K) for strength, 4.1% (in population Q) for uniformity, 45.8% (in population N) for elongation, and 13.9% (in population O) for lint percentage (lint%). While further testing for stability of the phenotype and estimation of yield potential is necessary, mutation breeding shows promise as an approach to reduce the problem of the genetic bottleneck of upland cotton. The populations developed here may also contribute to identifying candidate genes and causal mutations for fiber quality improvement.