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Introduction In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time. Materials and methods The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents. Conclusions Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.

Bread wheat (Triticum aestivum L.) is one of the world’s most important crops. Maintaining wheat yield gains across all of its major production areas is a key target toward underpinning global food security. Brazil is a major wheat producer in South America, generating grain yields of around 6.8 million tons per year. Here, we establish and genotype a wheat association mapping resource relevant to contemporary Brazilian wheat breeding programs. The panel of 558 wheat accessions was genotyped using an Illumina iSelect 90,000 single nucleotide polymorphism array. Following quality control, the final data matrix consisted of 470 accessions and 22,475 polymorphic genetic markers (minor allele frequency ≥5%, missing data <5%). Principal component analysis identified distinct differences between materials bred predominantly for the northern Cerrado region, compared to those bred for southern Brazilian agricultural areas. We augmented the genotypic data with 26 functional Kompetitive Allele-Specific PCR (KASP) markers to identify the allelic combinations at genes with previously known effects on agronomically important traits in the panel. This highlighted breeding targets for immediate consideration – notably, increased Fusarium head blight resistance via the Fhb1 locus. To demonstrate the panel’s likely future utility, genome-wide association scans for several phenotypic traits were undertaken. Significant (Bonferroni corrected P < 0.05) marker-trait associations were detected for Fusarium kernel damage (a proxy for type 2 Fusarium resistance), identifying previously known quantitative trait loci in the panel. This association mapping panel represents an important resource for Brazilian wheat breeding, allowing future genetic studies to analyze multiple agronomic traits within a single genetically diverse population.

There is an increasing interest in the development of uchuva (Physalis peruviana L.) cultivars adapted to greenhouse farming. Sexual behavior makes it difficult to obtain uniform commercial uchuva cultivars by conventional breeding methods. Mutations induced by gamma rays is an alternative approach. M1 plants derived from 14 irradiation 60Co doses, from 0 to 275 Gy, that were applied to uchuva seeds were evaluated. Recorded data included days to first flower and growth dynamics (four to seven samplings) of morphological traits (plant height, stem diameter, basal stems) and reproductive traits (floral buds, flowers and green fruits). Treatments were distributed in a completely randomized blocks experimental design with six replications, in a greenhouse. The experimental unit was a single M1 plant. Statistical differences were found for irradiation doses, growth samplings, and its interaction. Growth dynamics results indicate that all traits showed a linear increase with plant age (R2 = 0.92* to 0.98**), but the effect of the irradiation doses on morphological and reproductive traits was no linear. Irradiation reduced plant height by 79%. M1 plants developed from irradiated seeds at doses of 125, 175 and 200 Gy showed greater stem diameter, with more basal stems, floral buds, flowers and green fruits than the control. It is concluded that intermediate irradiation doses had a stimulating effect on vegetative growth and fruiting traits of M1 uchuva plants.

Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from ‘Green Revolution’ traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.

To face future challenges in crop production dictated by global climate changes, breeders and plant researchers collaborate to develop productive crops that are able to withstand a wide range of biotic and abiotic stresses. However, crop selection is often focused on shoot performance alone, as observation of root properties is more complex and asks for artificial and extensive phenotyping platforms. In addition, most root research focuses on development, while a direct link to the functionality of plasticity in root development for tolerance is often lacking. In this paper we review the currently known root system architecture (RSA) responses in Arabidopsis and a number of crop species to a range of abiotic stresses, including nutrient limitation, drought, salinity, flooding, and extreme temperatures. For each of these stresses, the key molecular and cellular mechanisms underlying the RSA response are highlighted. To explore the relevance for crop selection, we especially review and discuss studies linking root architectural responses to stress tolerance. This will provide a first step toward understanding the relevance of adaptive root development for a plant's response to its environment. We suggest that functional evidence on the role of root plasticity will support breeders in their efforts to include root properties in their current selection pipeline for abiotic stress tolerance, aimed to improve the robustness of crops.

LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performance in terms of prediction precision, model stability, and computing efficiency through a series of benchmark tests. We also assess the factors that are essential to ensure the best performance of genomic selection prediction by taking complex scenarios in crop hybrid breeding into account. LightGBM has been implemented as a toolbox, CropGBM, encompassing multiple novel functions and analytical modules to facilitate genomically designed breeding in crops.

Summary Transgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variation – and this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders’ ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.

ABSTRACT When an agricultural experiment is completed and the data about the response variable is available, it is necessary to perform an analysis of variance. However, the hypothesis testing of this analysis shows validity only if the assumptions of the statistical model are ensured. When such assumptions are violated, procedures must be applied to remedy the problem. The present study aimed to compare and investigate how the assumptions of the statistical model can be achieved by classical linear model and generalized linear mixed model, as well as their impact on the hypothesis test of the analysis of variance. The data used in this study was obtained from a genetic breeding program on the cooking time of segregating populations. The following solutions were proposed: i) Classical linear model with data transformation and ii) Generalized linear mixed models. The assumptions of normality and homogeneity were tested by Shapiro-Wilk and Levene, respectively. Both models were able to achieve the assumptions of the statistical model with direct impact on the hypothesis testing. The data transformations were effective in stabilizing the variance. However, several inappropriate transformations can be misapplied and meet the assumptions, which would distort the hypothesis test. The generalized linear mixed models may require more knowledge about the identification of lines of programming, compared to the classical method. However, besides the separation of fixed from random effects, they allow for the specification of the type of distribution of the response variable and the structuring of the residues.

In the coming decades, larger genetic gains in yield will be necessary to meet projected demand, and this must be achieved despite the destabilizing impacts of climate change on crop production. The root systems of crops capture the water and nutrients needed to support crop growth, and improved root systems tailored to the challenges of specific agricultural environments could improve climate resiliency. Each component of root initiation, growth and development is controlled genetically and responds to the environment, which translates to a complex quantitative system to navigate for the breeder, but also a world of opportunity given the right tools. In this review, we argue that it is important to know more about the ‘hidden half’ of crop plants and hypothesize that crop improvement could be further enhanced using approaches that directly target selection for root system architecture. To explore these issues, we focus predominantly on bread wheat (Triticum aestivum L.), a staple crop that plays a major role in underpinning global food security. We review the tools available for root phenotyping under controlled and field conditions and the use of these platforms alongside modern genetics and genomics resources to dissect the genetic architecture controlling the wheat root system. To contextualize these advances for applied wheat breeding, we explore questions surrounding which root system architectures should be selected for, which agricultural environments and genetic trait configurations of breeding populations are these best suited to, and how might direct selection for these root ideotypes be implemented in practice.

• Wheat (Triticum aestivum) is one of the most important crops worldwide. Given a growing global population coupled with increasingly challenging cultivation conditions, facilitating wheat breeding by fine-tuning important traits is of great importance. MADS-box genes are prime candidates for this, as they are involved in virtually all aspects of plant development. • Here, we present a detailed overview of phylogeny and expression of 201 wheat MIKC-type MADS-box genes. Homoeolog retention is significantly above the average genome-wide retention rate for wheat genes, indicating that many MIKC-type homoeologs are functionally important and not redundant. Gene expression is generally in agreement with the expected subfamily-specific expression pattern, indicating broad conservation of function of MIKC-type genes during wheat evolution. • We also found extensive expansion of some MIKC-type subfamilies, especially those potentially involved in adaptation to different environmental conditions like flowering time genes. Duplications are especially prominent in distal telomeric regions. A number of MIKC-type genes show novel expression patterns and respond, for example, to biotic stress, pointing towards neofunctionalization. • We speculate that conserved, duplicated and neofunctionalized MIKC-type genes may have played an important role in the adaptation of wheat to a diversity of conditions, hence contributing to the importance of wheat as a global staple food.