Comparing the genomes of two species offers a robust approach to unveil potentially overlooked genes that wield substantial influence on observed agronomic traits. Extensive phenotypic data from maize and sorghum were previously utilized in genome-wide association studies. This project aims to harness the potential of comparative genomics to strengthen confidence in these marker-trait associations and to suggest previously unexplored relationships. To achieve this goal, insights from studies on the genetically diverse Sorghum Association Panel and the maize Wisconsin Diversity Panel were leveraged to classify candidate genes as either shared between the two species or unique to one or the other. Candidate orthologous genes found in both species, associated with shared phenotypes, enhance the reliability of the associations within each species. Additionally, genes unique to each species provide parameters to inform future predictive models. Finally, given the ancient tetraploidy of maize and the biased loss of genes over time, the candidate genes identified in sorghum provide valuable information for understanding the orthologs found in the maize subgenomes.

Enhancing photosynthesis for increased sorghum grain yield has become a key focus in sorghum breeding efforts. Phenotyping, involving the measurement of various morpho-physiological and physical traits associated with photosynthesis and grain yield, is a time-intensive process. However, the potential of non-invasive leaf-level hyperspectral imaging to swiftly detect plant performance, optimizing photosynthesis and grain yield, is promising. This study aimed to evaluate the feasibility of utilizing hyperspectral reflectance in the 350–950 nm range for the rapid estimation of these traits in intact sorghum leaves. Multiple machine learning regression algorithms were developed using leaf-level hyperspectral reflectance data from nearly 400 sorghum accessions within an association panel. The best-performing prediction models were then considered as potential methods for constructing a prediction model targeting multiple other physiological and yield traits in sorghum accessions. The results indicate that this approach enables the early detection of leaf photosynthetic and yield traits through leaf-level hyperspectral reflectance without the need for a full-range, high-cost leaf spectrometer.

Maximizing crop yield is a central goal in agriculture and plant breeding. Several phenotypic traits, including leaf chlorophyll content and canopy greenness, can help predict yield across different varieties. In this study, we aimed to quantify the relationship between leaf chlorophyll concentration, canopy greenness, and crop yield within the Genomes to Fields trials. Our goal was to assess the correlation and enhance predictive modeling between these factors, evaluating the effectiveness of canopy cover and spectral reflectance in predicting yield. We accomplished this by collecting chlorophyll data from multiple leaves within each field plot and utilizing imagery captured by unoccupied aerial systems. This image data provided RGB and multispectral reflectance information on a per-plot basis throughout the growing season, allowing us to model patterns linking chlorophyll concentration and yield for each plot. We integrated data from these two methods with genotypic data from the same maize varieties to explore the fundamental relationships between chlorophyll levels, canopy greenness, and crop yield.

Corn is a cornerstone of the global food supply, but its vulnerability to unpredictable weather patterns, exacerbated by climate change, poses significant challenges to its consistent production. This study aims to enhance maize breeding efforts by identifying genetic markers associated with plant resilience, including the factors of growth rates, stress resistance, disease resistance, overall productivity, and variation in weather. Leveraging previous years of genetic, phenotypic, and drone imagery data from the maize Genomes to Fields project, we conducted a genetic analysis utilizing location-year datasets with varying disease presence, environmental stressors, and weather conditions. The focus was primarily on the anthesis-silking interval (ASI), an essential metric in drought resistance. Machine learning techniques were employed for efficient large-scale data analysis, hypothesis testing, and result interpretation. The outcomes of this research hold immense value for farmers grappling with seasonal droughts and extreme heat. Cultivating crops with a history of weather resilience can serve as a predictive tool, mitigating supply chain disruptions and food shortages, ultimately stabilizing food prices.

Understanding and enhancing plant resilience traits in maize is critical for ensuring consistent yields in the face of challenging conditions such as drought, disease, and insect pressure. The adoption of drought-resistant and insect-resistant maize varieties has already proven highly beneficial to growers, sparking further interest in uncovering the precise genetic and phenomic factors associated with increased resilience. This study utilized genetic, phenomic, and environmental data collected by Genomes to Fields teams across diverse regions of the United States. These regions exhibited a wide range of climatic conditions, from optimal to stressful, including drought and heat challenges. Leveraging advanced Machine Learning techniques, the study aimed to predict the impact of drought and heat stress on the anthesis-silking interval, a crucial trait influencing yield, and to identify the most effective combination of phenotypes for enhancing drought resilience in maize.

Tar spot disease in maize is the result of a fungal pathogen, Phyllachora maydis, and reduces yield up to 40 percent depending on severity of infection. Traditional disease monitoring techniques often suffer from limited coverage and time inefficiency. This study explores the application of drone-based imagery for phenomic prediction models in maize tar spot onset and severity detection. Using unoccupied aerial vehicles (UAVs) equipped with high-resolution cameras, multi-spectral and high-resolution images are captured of maize fields prior to disease onset and throughout the season. UAV images are processed and analyzed to extract essential phenotypic features related to plant health, including color, texture, size, and shape characteristics. The resulting data are integrated into machine learning algorithms to develop predictive models for disease detection and quantification. Tar spot detection and monitoring techniques would help with field management, minimizing overall yield loss caused by infection. The utilization of drone-based imagery for maize tar spot detection represents a significant advancement in precision agriculture, with the potential to revolutionize the way we monitor and manage crop health.

Plant growth and development is impacted by the ability to capture resources including sunlight, determined in part by the arrangement of plant parts throughout the canopy. This is a very complex trait to describe, but has a major impact on downstream traits such as biomass or grain yield per acre. Though some is known about genetic factors contributing to leaf angle, maturity, and leaf size and number, these discrete traits do not encompass the structural complexity of the canopy. In addition, modeling and prediction for plant developmental traits using genomics or phenomics are usually conducted separately. We have developed proof-of-concept models that incorporate spatio-temporal factors from drone-acquired LiDAR features in a maize diversity panel to predict plant growth and development over time to improve our understanding of the biology of canopy formation and development. Briefly, voxel models for probability of beam penetration into the foliage were generated from 3D LiDAR scans collected at seven dates throughout crop canopy development. From the same plots, key architectural features of the maize canopy were measured by hand: stand count; plant, tassel, and flag leaf height; anthesis and silking dates; ear leaf, total leaf, and largest leaf number; and largest leaf length and width. We develop a self-supervised autoencoding neural network architecture that separately encodes plant temporal growth patterns for individual genotypes and plant spatial distributions for each plot. Then, leveraging the resulting latent space encoding of the LiDAR scans, we train and demonstrate accurate prediction of hand-measured crop traits.

The expanding geographic range of Phyllachora maydis, the fungus that induces Tar Spot infection on corn foliage, is increasingly threatening a Michigan industry that contributes over $1 billion to the state’s economy annually. Advances in machine learning now enable quantification of crop infection presence and severity using powerful object detection packages such as Tensorflow, Keras, and more. Tensorflow, specifically, has developed Application Programming Interface (API) tools to connect powerful object detection capabilities with streamlined usability. Foliar infection of maize by P. maydis is often difficult to detect early. Visible lesions initially appear tiny, ambiguous, and sparse, making them difficult to identify with the naked eye. Both farmers and breeders of corn desperately need better tools that allow early, definitive detection of lesions and provide more time for management decisions. This tool must verify presence of P. maydis and quantify infection severity as quickly as possible to allow growers the most options for treatment. I propose a combination of supervised machine learning using Tensorflow for custom object detection, and containerized application-development software such as Docker to create a user interface accessible on desktop or mobile devices. This application will be developed by weaving the transferrable infrastructure of Docker with the powerful machine learning platforms Tensorflow and Tensorflow Lite, thereby allowing users to analyze images using their preferred operating system. By implementing both complementary Tensorflow platforms, farmers and breeders will be afforded the choice of either capturing and analyzing one image at a time, or detecting lesions continuously in real-time.