About

Cory Hirsch is an Associate Professor and Interim Department Head in the Department of Plant Pathology at the University of Minnesota. He holds a PhD in plant breeding and plant genetics from the University of Wisconsin-Madison and a BS in biochemistry from the same institution. His research focuses on plant stress resistance biology, particularly how plants respond to abiotic stresses such as extreme temperatures, salinity, and drought, as well as biotic stresses like pathogens and herbivores. His group utilizes enabling technologies to understand genomic and expression variation within species and how these variations relate to phenotypic traits, with the goal of increasing and sustaining plant health. Hirsch's work involves investigating plant responses to environmental stresses, the role of the microbiome in stress response, and plant pathogen interactions. His research employs approaches such as global gene expression analysis, genome architecture studies, metabolite profiling, and large-scale phenotyping. He also emphasizes understanding the limitations of data analysis technologies to enable accurate biological interpretations, especially in complex plant genomes.

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision

Selected publications

• Current methods and future needs for visible and non-visible detection of plant stress responses

  Frontiers in Plant Science · 2025-09-29 · 2 citations

  reviewOpen accessSenior authorCorresponding

  As climate change alters the frequency, intensity, and co-occurrences of abiotic and biotic stresses, the effective and efficient detection of plant stress responses and resistance mechanisms is critical for safeguarding global food security. Stressful environments elicit both visible and non-visible changes in plants. Cellular and subcellular changes, often invisible to the naked eye, can serve as indicators of stress and can be quantified using molecular, ionomic, metabolomic, genomic, and transcriptomic methods. In contrast, visible responses such as discoloration, morphological changes, and disease symptoms can be monitored efficiently through atmospheric, aerial, and terrestrial remote sensing platforms. Phenotyping at the whole-plant and organ levels offers valuable insights for diagnosing stress in situ , providing opportunities to study plant resistance and acclimation strategies under realistic conditions. However, the complexity of plant stress responses, spanning microscopic to macroscopic scales and diverse biological processes, make it challenging for any single technology to comprehensively capture the full spectrum of reactions. Furthermore, the rising prevalence of multifactorial stress conditions highlights the need for research on synergistic and antagonistic interactions between stress factors. To effectively mitigate the impacts of stress on agriculture, future research must prioritize integrative multi-omic approaches that connect cellular and subcellular processes with morphological and phenological stress responses.

  PublisherOA PDFDOI

• Plant height defined growth curves can predict end of season maize yield

  The Plant Phenome Journal · 2025-05-29 · 1 citations

  articleOpen access

  Abstract The development of quick, easy, and low‐cost methods to quantify within‐field variation is essential to successful implementation of mid‐season management for precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict variation in end of season grain yield throughout the field. To test this, image‐based plant height data were collected weekly from planting to flowering in production maize fields to assess within‐field variation in growth curves and the relationship with grain yield. A different commercial hybrid was grown under standard production conditions in each year of the experiment to assess the generalizability of the model. Plant height and growth rate had variable correlation with grain yield depending on the time point and growth environment ( r = −0.61 to 0.76). A partial least squares model trained using temporal growth rate predicted within field grain yield variation with an average correlation of r = 0.46 across years. Insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the training data used for model development. In the future, with more training data from a range of stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within‐field variation, and has application in making mid‐season management decisions.

  PublisherOA PDFDOI

• Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-06-29

  preprintOpen accessCorresponding

  Abstract Precision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.

  PublisherOA PDFDOI

• Optimized Methods for Applying and Assessing Heat, Drought, and Nutrient Stress of Maize Seedlings in Controlled Environment Experiments

  Cold Spring Harbor Protocols · 2024-10-16 · 2 citations

  reviewSenior author

  ), also known as corn, is an important crop that plays a crucial role in global agriculture. The economic uses of maize are numerous, including for food, feed, fiber, and fuel. It has had a significant historical importance in research as well, with important discoveries made in maize regarding plant domestication, transposons, heterosis, genomics, and epigenetics. Unfortunately, environmental stresses cause substantial yield loss to maize crops each year. Yield losses are predicted to increase in future climate scenarios, posing a threat to food security and other sectors of the global economy. Developing efficient methods to study maize abiotic stress responses is a crucial step toward a more resilient and productive agricultural system. This review describes the importance of and methods for studying the effects of heat, drought, and nutrient deficiency on early developmental stages of maize grown in controlled environments. Studying the early effects of environmental stressors in controlled environments allows researchers to work with a variety of environmental conditions with low environmental variance, which can inform future field-based research. We highlight the current knowledge of physiological responses of maize to heat, drought, and nutrient stress; remaining knowledge gaps and challenges; and information on how standardized protocols can address these issues.

  PublisherDOI

• Virulence is not directly related to strain success <i>in planta</i> in <i>Clavibacter nebraskensis</i>

  mSystems · 2024-11-29 · 2 citations

  articleOpen access

  ABSTRACT Goss’s wilt and leaf blight of maize is an economically important disease caused by the Gram-positive bacterium, Clavibacter nebraskensis ( Cn ). Little is known about the ecology and pathogenesis of this bacterium. Here, we used phenotypic assays and a high-throughput whole-genome sequencing approach to explore among-strain variation in virulence and multistrain reproductive success in planta . Our survey of 41 strains revealed that more recently sampled strains tended to have higher virulence than strains sampled before 2010 and tended to be more genetically divergent from the reference strain, isolated in 1971. More detailed assays with a representative sample of 13 of these strains revealed that host genotype (resistant or susceptible) did not strongly affect strain success and that strain success in planta in multi-strain communities was not closely associated with virulence in single-strain assays. Two weakly virulent strains, CIC354 and CIC370, had the greatest reproductive success, whereas the most highly virulent strains did not significantly change in frequency in any host genotype. A genomic analysis revealed candidate genes, including putative virulence factors (i.e., a secreted cellulase), responsible for among-strain variation in reproductive success. IMPORTANCE Non-pathogenic strains of many bacterial pathogens are reported to coexist with pathogenic strains in symptomatic plants. To understand the ecology and pathogenesis of the pathogen population, it is essential to study strain dynamics in the context of the host. We created a community of 13 strains exhibiting diverse virulence phenotypes and used this community to infect the host plant. We compared the strain frequency of these strains before and after the host infection. Contrary to our hypothesis of highly virulent strains being selected by the susceptible host, we found that weakly virulent strains were selected by both resistant and susceptible host lines. We identified several genes associated with strain frequency shifts suggesting their role in strain colonization, virulence, and fitness.

  PublisherDOI

• Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

  The Plant Journal · 2024-10-27 · 2 citations

  articleOpen access

  Plant height can be an indicator of plant health across environments and used to identify superior genotypes. Typically plant height is measured at a single timepoint when plants reach terminal height. Evaluating plant height using unoccupied aerial vehicles allows for measurements throughout the growing season, facilitating a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected from planting until terminal height at anthesis (14 flights 2018, 27 in 2019, 12 in 2020, and 11 in 2021) for a panel of ~500 diverse maize inbred lines. The percent variance explained in plant height throughout the season was significantly explained by genotype (9-48%), year (4-52%), and genotype-by-year interactions (14-36%) to varying extents throughout development. Genome-wide association studies revealed 717 significant single nucleotide polymorphisms associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth. When plant height growth curves were compared to growth curves estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

  PublisherDOI

• Temporally resolved growth patterns reveal novel information about the polygenic nature of complex quantitative traits

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-07-02

  preprintOpen access

  Abstract Plant height can be an indicator of plant health across environments and used to identify superior genotypes or evaluate abiotic stress factors. Typically plant height is measured at a single time point when plants have reached terminal height for the season. Evaluating plant height using unoccupied aerial vehicles (UAVs) is faster, allowing for measurements throughout the growing season, which facilitates a better understanding of plant-environment interactions and the genetic basis of this complex trait. To assess variation throughout development, plant height data was collected weekly for a panel of ∼500 diverse maize inbred lines over four growing seasons. The variation in plant height throughout the season was significantly explained by genotype, year, and genotype-by-year interactions to varying extents throughout development. Genome-wide association studies revealed significant SNPs associated with plant height and growth rate at different parts of the growing season specific to certain phases of vegetative growth that would not be identified by terminal height associations alone. When plant height growth rates were compared to growth rates estimated from canopy cover, greater Fréchet distance stability was observed in plant height growth curves than for canopy cover. This indicated canopy cover may be more useful for understanding environmental modulation of overall plant growth and plant height better for understanding genotypic modulation of overall plant growth. This study demonstrated that substantial information can be gained from high temporal resolution data to understand how plants differentially interact with the environment and can enhance our understanding of the genetic basis of complex polygenic traits.

  PublisherOA PDFDOI

• Maize Abiotic Stress Treatments in Controlled Environments

  Cold Spring Harbor Protocols · 2024-10-16 · 2 citations

  articleSenior author

  ) is one of the world's most important crops, providing food for humans and livestock and serving as a bioenergy source. Climate change and the resulting abiotic stressors in the field reduce crop yields, threatening food security and the global economy. Water deficit (i.e., drought), heat, and insufficient nutrients (e.g., nitrogen and phosphorus) are major environmental stressors that affect maize yields, and impact growth and development at all stages of the plant life cycle. Understanding the biological processes underlying these responses in maize has the potential to increase yields in the face of abiotic stress. Optimizing individual or combined abiotic stress treatments in controlled environments reduces potential noise in data collection that can be present under less controlled growth conditions. Here, we describe methods and conditions for controlled abiotic stress treatments and associated controls during early vegetative growth of maize, conducted in greenhouses or growth chambers. This includes the environmental conditions, equipment, soil preparation, and intensity and duration of heat, drought, nitrogen deficiency, and phosphorous deficiency. Controlled experiments at early growth stages are informative for future in-field studies that require greater labor and inputs, saving researchers time and growing space, and thus research funds, before testing plants across later stages of development. We suggest that stress treatments be severe enough to result in a measurable phenotype, but not so severe that all plants die prior to sample collection. This protocol is designed to set important standards for replicable research in maize.

  PublisherDOI

• Exploring Maize Stress Response Phenotypes via High-Throughput Imaging

  2023-10-18

  preprint

  This study aimed to investigate the physiological and morphological responses of maize seedlings to environmental stressors. High-throughput imaging analysis was used to characterize the stress response phenotypes of 47 distinct maize genotypes exposed to water limitation (40% field capacity), heat (38 °C), and the combination of the two stresses. RGB and NIR images were collected daily, and analyzed with open-source, open-development software PlantCV. Our investigation focused on quantitative measurements of daily area, daily water loss, evaluation of estimated water use efficiency (WUE), and near-infrared (NIR) reflectance as an estimation of water content in tissues. Quantitatively comparing two non-normal distributions, like the NIR histogram data, can be challenging but important, since metrics like median mode values often do not capture variation across a sample. One of the primary obstacles lies in defining an appropriate metric to accurately quantify the variation between two distributions. To analyze NIR reflectance, we evaluated the dissimilarity between pairwise NIR histograms by utilizing the earth mover’s distance (EMD) analysis. The EMD quantifies the difference between two NIR reflectance distributions, and therefore indirectly evaluates the difference in leaf water content for stress vs control conditions. Overall, most genotypes displayed growth reduction under drought and heat. Interestingly, specific lines exhibited heightened WUE under water limitation, suggesting response to water scarcity. EMD results showing dissimilarities in pairwise NIR reflectance of control vs drought, can be used to describe variation in dynamic changes in water content levels among these groups.

  PublisherDOI

• Predicting alpha amylase content and starch breakdown in pregerminated barley seeds using hyperspectral imaging

  2023-10-11

  preprint

  Malting is the process of controlled germination of cereal grain that is steeped, germinated, and dry kilned to develop flavor compounds, fermentable sugars, the hydrolytic enzymes necessary for the brewing process. To meet the strict standards of the brewing industry, variation in the germination of barley grains need to be minimal but also maintain rapid germination and optimum levels of a and B amylase. Preharvest sprouting poses a significant threat to barley cultivars prior to harvest, resulting in premature endosperm modification, reduced enzyme content, and poor malthouse performance. Pregerminated grain is difficult to detect as there are often no signs of damage, and current methods such as the Hagberg falling/Stirring number, or pearling tests, each of which ultimately leads to the destruction of the seed. Here we used time-series hyperspectral imaging of barley seeds undergoing active and or pregerminated seed and used a deep neural network to predict stirring number and alpha amylase values, which are proxy measures for sprout damage. Prediction models were made for seven genotypes tested individually and with all genotypes combined. Stirring number assays ranged from 0 to 190 and our prediction models had mean average errors of 10.5 to 23.9 depending on the model. To increase the resolution and accuracy of the predictions we transitioned from using bulk conventional molecular assays to single seed assays and prediction models. These results demonstrate that hyperspectral imaging and machine learning models can be used to predict germinated grain in bulk and single seed assays.

  PublisherDOI

Frequent coauthors

• Shawn M. Kaeppler

  University of Wisconsin–Madison

  74 shared

• Nathan M. Springer

  Bayer (United States)

  69 shared

• Natalia de León

  University of Wisconsin–Madison

  63 shared

• C. Robin Buell

  58 shared

• Sherry Flint‐Garcia

  United States Department of Agriculture

  34 shared

• Rajandeep S. Sekhon

  Clemson University

  26 shared

• Alex B. Brohammer

  25 shared

• Edward S. Buckler

  Cornell University

  25 shared

Labs

• Hirsch LabPI

Education

• Plant Breeding and Plant Genetics, PhD, Horticulture Department

  University of Wisconsin Madison

  2011

• Biochemistry, B.S.

  University of Wisconsin Madison

  2005