Accelerate Crop Improvement Research Articles

A CRISPR way for accelerating cereal crop improvement: Progress and challenges.

Humans rely heavily on cereal grains as a key source of nutrients, hence regular improvement of cereal crops is essential for ensuring food security. The current food crisis at the global level is due to the rising population and harsh climatic conditions which prompts scientists to develop smart resilient cereal crops to attain food security. Cereal crop improvement in the past generally depended on imprecise methods like random mutagenesis and conventional genetic recombination which results in high off targeting risks. In this context, we have witnessed the application of targeted mutagenesis using versatile CRISPR-Cas systems for cereal crop improvement in sustainable agriculture. Accelerated crop improvement using molecular breeding methods based on CRISPR-Cas genome editing (GE) is an unprecedented tool for plant biotechnology and agriculture. The last decade has shown the fidelity, accuracy, low levels of off-target effects, and the high efficacy of CRISPR technology to induce targeted mutagenesis for the improvement of cereal crops such as wheat, rice, maize, barley, and millets. Since the genomic databases of these cereal crops are available, several modifications using GE technologies have been performed to attain desirable results. This review provides a brief overview of GE technologies and includes an elaborate account of the mechanisms and applications of CRISPR-Cas editing systems to induce targeted mutagenesis in cereal crops for improving the desired traits. Further, we describe recent developments in CRISPR-Cas-based targeted mutagenesis through base editing and prime editing to develop resilient cereal crop plants, possibly providing new dimensions in the field of cereal crop genome editing.

The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement.

Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.

Optimized prime editing in monocot plants using PlantPegDesigner and engineered plant prime editors (ePPEs).

Prime editors (PEs), which can install desired base edits without donor DNA or double-strand breaks, have been used in plants and can, in principle, accelerate crop improvement and breeding. However, their editing efficiency in plants is generally low. Optimizing the prime editing guide RNA (pegRNA) by designing the sequence on the basis of melting temperature, using dual-pegRNAs and engineering PEs have all been shown to enhance PE efficiency. In addition, an automated pegRNA design platform, PlantPegDesigner, has been developed on the basis of rice prime editing experimental data. In this protocol, we present detailed protocols for designing and optimizing pegRNAs using PlantPegDesigner, constructing engineered plant PE vectors with enhanced editing efficiency for prime editing, evaluating prime editing efficiencies using a reporter system and comparing the effectiveness and byproducts of PEs by deep amplicon sequencing. Using this protocol, researchers can construct optimized pegRNAs for prime editing in 4-7 d and obtain prime-edited rice or wheat plants within 3 months.

Editing a healthier future in plants

CRISPR genome editing tools consist of a small RNA and a single protein that together can locate, bind, and cut a specific sequence in a complex genome. There are now many versions of CRISPR editing tools that can modify their targets in different ways, including with sequence insertions, deletions, and specific base changes. As CRISPR-based therapies advance through clinical trials, genome editing tools are also enabling significant progress in crop plants important to global agriculture. They are used by plant breeders to precisely introduce specific genetic variation at genes that underlie important traits such as flavor, shelf life, yield, or disease resistance, thereby accelerating crop improvement.

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Optimization of Prime Editing in Rice, Peanut, Chickpea, and Cowpea Protoplasts by Restoration of GFP Activity.

Precise editing of the plant genome has long been desired for functional genomic research and crop breeding. Prime editing is a newly developed precise editing technology based on CRISPR-Cas9, which uses an engineered reverse transcriptase (RT), a catalytically impaired Cas9 endonuclease (nCas9), and a prime editing guide RNA (pegRNA). In addition, prime editing has a wider range of editing types than base editing and can produce nearly all types of edits. Although prime editing was first established in human cells, it has recently been applied to plants. As a relatively new technique, optimization will be needed to increase the editing efficiency in different crops. In this study, we successfully edited a mutant GFP in rice, peanut, chickpea, and cowpea protoplasts. In rice, up to 16 times higher editing efficiency was achieved with a dual pegRNA than the single pegRNA containing vectors. Edited-mutant GFP protoplasts have also been obtained in peanut, chickpea, and cowpea after transformation with the dual pegRNA vectors, albeit with much lower editing efficiency than in rice, ranging from 0.2% to 0.5%. These initial results promise to expedite the application of prime editing in legume breeding programs to accelerate crop improvement.

Recent Advances in DNA Methylation and Their Potential Breeding Applications in Plants

Traditional plant breeding encompasses repetitive crossing and selection based on morphological traits, while phenotypic selection has been complemented by molecular methods in recent decades. Genome editing with techniques like the CRISPR-Cas9 system is still a novel approach that is being used to make direct modifications to nucleotide sequences of crops. In addition to these genetic alterations, an improved understanding of epigenetic variations such as DNA methylation on the phenotype of plants has led to increased opportunities to accelerate crop improvement. DNA methylation is the most widely studied epigenetic mark in plants and other eukaryotes. These epigenetic marks are highly conserved and involved in altering the activities and functions of developmental signals by catalyzing changes in the chromatin structure through methylation and demethylation. Cytosine methylation (5mC) is the most prevalent modification found in DNA. However, recent identification of N6-methyladenosine (6mA) in plants starts to reveal their critical role in plant development. Epigenetic modifications are actively involved in creating the phenotype by controlling essential biological mechanisms. Epigenetic modifications could be heritable and metastable causing variation in epigenetic status between or within species. However, both genetic and heritable epigenetic variation has the potential to drive natural variation. Hence, epigenome editing might help overcome some of the shortcomings of genome editing (such as gene knockout), which can have significant off-target effects and only enables the loss of a gene’s function. In this review, we have discussed the mechanism underlying DNA methylation and demethylation in plants. Methyltransferases and demethylases are involved in catalyzing specific types of modification. We also discuss the potential role of DNA modifications in crop improvement for meeting the requirements of sustainable and green agriculture.

Functional Allele Validation by Gene Editing to Leverage the Wealth of Genetic Resources for Crop Improvement.

Advances in molecular technologies over the past few decades, such as high-throughput DNA marker genotyping, have provided more powerful plant breeding approaches, including marker-assisted selection and genomic selection. At the same time, massive investments in plant genetics and genomics, led by whole genome sequencing, have led to greater knowledge of genes and genetic pathways across plant genomes. However, there remains a gap between approaches focused on forward genetics, which start with a phenotype to map a mutant locus or QTL with the goal of cloning the causal gene, and approaches using reverse genetics, which start with large-scale sequence data and work back to the gene function. The recent establishment of efficient CRISPR-Cas-based gene editing promises to bridge this gap and provide a rapid method to functionally validate genes and alleles identified through studies of natural variation. CRISPR-Cas techniques can be used to knock out single or multiple genes, precisely modify genes through base and prime editing, and replace alleles. Moreover, technologies such as protoplast isolation, in planta transformation, and the use of developmental regulatory genes promise to enable high-throughput gene editing to accelerate crop improvement.

Speed breeding to accelerate crop improvement

Global food security has become a major issue as the human population grows and the environment changes, with the current rate of improvement of several important crops inadequate to meet future demand. Crop plants have extended generation times, which contributes to the slow rate of progress. However, speed breeding has revolutionized the entire world by reducing generation time and speeding up breeding and research programs to improve crop varieties. In the absence of an integrated pre-breeding program, breeding new and high-performing cultivars with market-preferred traits can take more than ten years. After the first cross with parental genotypes, a large amount of time, space, and resources are committed to the selection and genetic advancement stages during the early stages of breeding. Speed breeding has the ability to shorten the time it takes to develop, market, and commercialize cultivars. Crop improvement in the face of a fast-changing environment and an ever-increasing human population is a major concern for scientists around the world. Current crop enhancement projects are progressing at a rate that is insufficient to meet food demand. Crop redesign is urgently needed for climate resilience, as well as long-term yield and nutrition. Crop progress is slowed significantly by the long generation time required by crop plants during the breeding process. Speed breeding is now being used on a large scale to shorten generation time and support multiple crop generations per year as a solution in this approach. Researchers are now using an integrated approach to improve breeding efficiency, combining speed breeding with current plant breeding and genetic engineering methods. Speed breeding is a promising approach for achieving nutritional security and sustainable agriculture by shortening breeding cycles for food and industrial crop enhancement. Speed breeding is a methodology that allows plant breeders to improve crop production by adjusting temperature, light duration, and intensity to boost plant development. It uses an artificial source of light, which is kept on continuously, to activate the photosynthetic process, which leads to growth and reproduction much earlier than normal. This will assist in meeting the demands of the future’s rising population. This can be accomplished using a variety of technologies, including genotyping, marker-assisted selection, high throughput phenotyping; gene editing, genomic selection, and re-domestication, all of which can be combined with speed breeding to allow plant breeders to keep up with a changing climate and growing human population.

Increase Crop Resilience to Heat Stress Using Omic Strategies.

Varieties of various crops with high resilience are urgently needed to feed the increased population in climate change conditions. Human activities and climate change have led to frequent and strong weather fluctuation, which cause various abiotic stresses to crops. The understanding of crops’ responses to abiotic stresses in different aspects including genes, RNAs, proteins, metabolites, and phenotypes can facilitate crop breeding. Using multi-omics methods, mainly genomics, transcriptomics, proteomics, metabolomics, and phenomics, to study crops’ responses to abiotic stresses will generate a better, deeper, and more comprehensive understanding. More importantly, multi-omics can provide multiple layers of information on biological data to understand plant biology, which will open windows for new opportunities to improve crop resilience and tolerance. However, the opportunities and challenges coexist. Interpretation of the multidimensional data from multi-omics and translation of the data into biological meaningful context remained a challenge. More reasonable experimental designs starting from sowing seed, cultivating the plant, and collecting and extracting samples were necessary for a multi-omics study as the first step. The normalization, transformation, and scaling of single-omics data should consider the integration of multi-omics. This review reports the current study of crops at abiotic stresses in particular heat stress using omics, which will help to accelerate crop improvement to better tolerate and adapt to climate change.

Coexpression of MEIOTIC-TOPOISOMERASE VIB-dCas9 with guide RNAs specific to a recombination hotspot is insufficient to increase crossover frequency in Arabidopsis.

During meiosis, homologous chromosomes pair and recombine, which can result in reciprocal crossovers that increase genetic diversity. Crossovers are unevenly distributed along eukaryote chromosomes and show repression in heterochromatin and the centromeres. Within the chromosome arms, crossovers are often concentrated in hotspots, which are typically in the kilobase range. The uneven distribution of crossovers along chromosomes, together with their low number per meiosis, creates a limitation during crop breeding, where recombination can be beneficial. Therefore, targeting crossovers to specific genome locations has the potential to accelerate crop improvement. In plants, meiotic crossovers are initiated by DNA double-strand breaks that are catalyzed by SPO11 complexes, which consist of 2 catalytic (SPO11-1 and SPO11-2) and 2 noncatalytic subunits (MTOPVIB). We used the model plant Arabidopsis thaliana to coexpress an MTOPVIB-dCas9 fusion protein with guide RNAs specific to the 3a crossover hotspot. We observed that this was insufficient to significantly change meiotic crossover frequency or pattern within 3a. We discuss the implications of our findings for targeting meiotic recombination within plant genomes.

Recent advancements in CRISPR/Cas technology for accelerated crop improvement.

Precise genome engineering approaches could be perceived as a second paradigm for targeted trait improvement in crop plants, with the potential to overcome the constraints imposed by conventional CRISPR/Cas technology. The likelihood of reduced agricultural production due to highly turbulent climatic conditions increases as the global population expands. The second paradigm of stress-resilient crops with enhanced tolerance and increased productivity against various stresses is paramount to supportglobal production and consumption equilibrium. Although traditional breeding approaches have substantially increased crop production and yield, effective strategies are anticipated to restore crop productivity even further in meeting the world's increasing food demands. CRISPR/Cas, which originated in prokaryotes, has surfaced as a coveted genome editing tool in recent decades, reshaping plant molecular biology in unprecedented ways and paving the way for engineering stress-tolerant crops. CRISPR/Cas is distinguished by its efficiency, high target specificity, and modularity, enables precise genetic modification of crop plants, allowing for the creation of allelic variations in the germplasm and the development of novel and more productive agricultural practices. Additionally, a slew of advanced biotechnologies premised on the CRISPR/Cas methodologies have augmented fundamental research and plant synthetic biology toolkits. Here, we describe gene editing tools, including CRISPR/Cas and its imitative tools, such as base and prime editing, multiplex genome editing, chromosome engineering followed by their implications in crop genetic improvement. Further, we comprehensively discuss the latest developments of CRISPR/Cas technology including CRISPR-mediated gene drive, tissue-specific genome editing, dCas9 mediated epigenetic modification and programmed self-elimination of transgenes in plants. Finally, we highlight the applicability and scope of advanced CRISPR-based techniques in crop genetic improvement.

Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction

Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few approaches for genotype to phenotype prediction compare machine learning with deep learning and further interpret the models that support the predictions. This study uses genome wide molecular markers and traits across 1110 soybean individuals to develop accurate prediction models. For 13/14 sets of predictions, XGBoost or random forest outperformed deep learning models in prediction performance. Top ranked SNPs by F-score were identified from XGBoost, and with further investigation found overlap with significantly associated loci identified from GWAS and previous literature. Feature importance rankings were used to reduce marker input by up to 90%, and subsequent models maintained or improved their prediction performance. These findings support interpretable machine learning as an approach for genomic based prediction of traits in soybean and other crops.

Principles of Nanoparticle Design for Genome Editing in Plants.

Precise plant genome editing technologies have provided new opportunities to accelerate crop improvement and develop more sustainable agricultural systems. In particular, the prokaryote-derived CRISPR platforms allow precise manipulation of the crop genome, enabling the generation of high-yielding and stress-tolerant crop varieties. Nanotechnology has the potential to catalyze the development of a novel molecular toolbox even further by introducing the possibility of a rapid, universal delivery method to edit the plant genome in a species-independent manner. In this Perspective, we highlight how nanoparticles can help unlock the full potential of CRISPR/Cas technology in targeted manipulation of the plant genome to improve agricultural output. We discuss current challenges hampering progress in nanoparticle-enabled plant gene-editing research and application in the field, and highlight how rational nanoparticle design can overcome them. Finally, we examine the implications of the regulatory frameworks and social acceptance for the future of nano-enabled precision breeding in the developing world.

Market Intelligence and Incentive-Based Trait Ranking for Plant Breeding: A Sweetpotato Pilot in Uganda.

Crop breeding programs must accelerate crop improvement, spur widespread adoption of new varieties and increase variety turnover they are to meet the diverse needs of their clients. More comprehensive quantitative approaches are needed to better inform breeding programs about the preferred traits among farmers and other actors. However, the ability of current breeding programs to meet the demands of their clients is limited by the lack of insights about value chain actor preference for individual or packages of traits. Ranking traits based on monetary incentives, rather than subjective values, represents a more comprehensive, consistent, and quantitative approach to inform breeding programs. We conducted a large pilot in Uganda to assess the implementation of a novel approach to trait ranking, using a uniquely large sample of diverse sweetpotato value chain actors. We found meaningful differences in trait ranking and heterogeneity among different actors using this approach. We also show our approach’s effectiveness at uncovering unmet demand for root quality traits and at characterizing the substantial trait demand heterogeneity among value chain players. Implementing this approach more broadly for sweetpotato and other crops would increase the effectiveness of breeding programs to improve food security in developing countries.

Evidence for the Application of Emerging Technologies to Accelerate Crop Improvement - A Collaborative Pipeline to Introgress Herbicide Tolerance Into Chickpea.

Accelerating genetic gain in crop improvement is required to ensure improved yield and yield stability under increasingly challenging climatic conditions. This case study demonstrates the effective confluence of innovative breeding technologies within a collaborative breeding framework to develop and rapidly introgress imidazolinone Group 2 herbicide tolerance into an adapted Australian chickpea genetic background. A well-adapted, high-yielding desi cultivar PBA HatTrick was treated with ethyl methanesulfonate to generate mutations in the ACETOHYDROXYACID SYNTHASE 1 (CaAHAS1) gene. After 2 years of field screening with imidazolinone herbicide across >20 ha and controlled environment progeny screening, two selections were identified which exhibited putative herbicide tolerance. Both selections contained the same single amino acid substitution, from alanine to valine at position 205 (A205V) in the AHAS1 protein, and KASP™ markers were developed to discriminate between tolerant and intolerant genotypes. A pipeline combining conventional crossing and F2 production with accelerated single seed descent from F2:4 and marker-assisted selection at F2 rapidly introgressed the herbicide tolerance trait from one of the mutant selections, D15PAHI002, into PBA Seamer, a desi cultivar adapted to Australian cropping areas. Field evaluation of the derivatives of the D15PAHI002 × PBA Seamer cross was analyzed using a factor analytic mixed model statistical approach designed to accommodate low seed numbers resulting from accelerated single seed descent. To further accelerate trait introgression, field evaluation trials were undertaken concurrent with crop safety testing trials. In 2020, 4 years after the initial cross, an advanced line selection CBA2061, bearing acetohydroxyacid synthase (AHAS) inhibitor tolerance and agronomic and disease resistance traits comparable to parent PBA Seamer, was entered into Australian National Variety Trials as a precursor to cultivar registration. The combination of cross-institutional collaboration and the application of novel pre-breeding platforms and statistical technologies facilitated a 3-year saving compared to a traditional breeding approach. This breeding pipeline can be used as a model to accelerate genetic gain in other self-pollinating species, particularly food legumes.

Can We Harness "Enviromics" to Accelerate Crop Improvement by Integrating Breeding and Agronomy?

The diverse consequences of genotype-by-environment (GxE) interactions determine trait phenotypes across levels of biological organization for crops, challenging our ambition to predict trait phenotypes from genomic information alone. GxE interactions have many implications for optimizing both genetic gain through plant breeding and crop productivity through on-farm agronomic management. Advances in genomics technologies have provided many suitable predictors for the genotype dimension of GxE interactions. Emerging advances in high-throughput proximal and remote sensor technologies have stimulated the development of “enviromics” as a community of practice, which has the potential to provide suitable predictors for the environment dimension of GxE interactions. Recently, several bespoke examples have emerged demonstrating the nascent potential for enhancing the prediction of yield and other complex trait phenotypes of crop plants through including effects of GxE interactions within prediction models. These encouraging results motivate the development of new prediction methods to accelerate crop improvement. If we can automate methods to identify and harness suitable sets of coordinated genotypic and environmental predictors, this will open new opportunities to upscale and operationalize prediction of the consequences of GxE interactions. This would provide a foundation for accelerating crop improvement through integrating the contributions of both breeding and agronomy. Here we draw on our experience from improvement of maize productivity for the range of water-driven environments across the US corn-belt. We provide perspectives from the maize case study to prioritize promising opportunities to further develop and automate “enviromics” methodologies to accelerate crop improvement through integrated breeding and agronomic approaches for a wider range of crops and environmental targets.

Generation of a high-efficiency adenine base editor with TadA8e for developing wheat dinitroaniline-resistant germplasm

Base editing using CRISPR technologies is an invaluable tool for crop breeding. One of the major base editors, the adenine base editor (ABE), has been successfully used in both model plants and many crops. However, owing to limited editing efficiency, the ABE has been difficult to apply in polyploid crops such as allohexaploid bread wheat that often require simultaneous mutation of multiple alleles for fast breeding. We have designed a wheat high-efficiency ABE (WhieABE), using the newly developed high-activity adenosine deaminase TadA8e. In vivo and in vitro analysis demonstrated the improved applicability of TadA8e over the commonly used TadA7.10. Dinitroaniline is a widely used herbicide with high effectiveness and low toxicity to animals. However, wheat cultivars with tolerance to dinitroaniline are rare, limiting the application of dinitroaniline in wheat planting. Using A-to-G editing with WhieABE, we found that a Met-to-Thr mutation in wheat tubulin alleles located on chromosomes 1A, 1B, 1D, 4A, and 4D increased the resistance of wheat to dinitroaniline, revealing a dosage effect of edited tubulins in resistance. The WhieABE promises to be a valuable editing tool for accelerating crop improvement and developing herbicide-resistant wheat germplasm.

Next-Generation Breeding Strategies for Climate-Ready Crops.

Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.

Resources for image-based high-throughput phenotyping in crops and data sharing challenges.

High-throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors, such as red green and blue (RGB) cameras, hyperspectral sensors, and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterize crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalizing between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation, and quantitative trait measurement. We emphasize the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.