Wednesday, April 9^th, 2025

Agronomy 3140, 3:00 – 4:00 pm

“Genetic Insights into Mung Bean Protein Content”

Ashlyn Rairdin,

Seminar Description: Mung bean (Vigna radiata (L.) Wizcek) is a promising crop for the plant-based protein market, yet limited research has been conducted on its protein content in the United States. To support domestic breeding efforts, this study integrates genomic analysis with the Iowa mungbean breeding program to identify loci associated with protein content and facilitate marker-assisted selection. A diverse panel of 375 genotypes, representing accessions from multiple countries, was evaluated across three environments. Protein content and moisture were quantified using wet chemistry, and genome-wide association analyses were conducted. Significant single nucleotide polymorphisms (SNPs) were identified, suggesting potential candidate regions for selection. These findings provide valuable insights into the genetic architecture of protein content in mung bean and support the development of improved varieties tailored to U.S. production systems.

Ashlyn Rairdin is a Ph.D. student in Plant Breeding at Iowa State University, specializing in Predictive Plant Phenomics under Dr. Arti Singh. Her research focuses on high-throughput phenotyping for protein content and yield prediction in mung bean using drone-based imagery. Beyond research, she has served as president of the Predictive Plant Phenomics graduate student organization and held leadership roles in the R.F. Baker Plant Breeding Committee. She is also a lead organizer for the Workforce in Ag and AI group, coordinating outreach efforts to engage students and the public in agricultural technology. Ashlyn is interested in opportunities that allow her to develop and apply phenotyping tools to support plant breeding and research.

"Data-Driven Soybean Yield Prediction Using Soil, Ground Hyperspectral, UAV, and Satellite Multispectral Data"

Joscif Raigne,

Seminar Description: Predicting soybean yield accurately is a complex challenge in plant breeding due to various interacting factors. This study addresses this challenge by performing comprehensive analysis using multiple data sources: soil data, ground-based hyperspectral reflectance, uncrewed aerial vehicle (UAV)-based multispectral reflectance, and satellite-based multispectral reflectance data. This research aims to understand which data collection scenarios offer the best opportunities for predicting end-season seed yield with high accuracy. This study employed data-driven learning and machine learning models, including random forest regression, along with feature selection techniques to identify the most informative features from each data source. By evaluating the contributions of each data source, this research highlights the potential of integrating remote sensing technologies and soil data to optimize data-driven approaches for soybean yield prediction and breeding decisions in large-scale programs.

Joscif G. Raigne is a Ph.D. student in Plant Breeding at Iowa State University under the guidance of Dr. Asheesh K. Singh (Danny). He completed his bachelor’s in biotechnologies at Utah Valley University and his master’s in plant breeding at Iowa State University also under Dr. Singh.  He has extensive experience in high-throughput phenotyping, remote sensing, and data analytics, applying drone and satellite-based tools to improve selection efficiency in large-scale breeding trials.

 Through his work, Joscif develops workflows that integrate UAV flights, sensor data such as RGB, LiDAR, and multispectral, and modeling platforms such as APSIM to optimize decisions in cultivar development to accelerate breeding progress and address emerging challenges in agriculture.

He has led projects focusing on soybean biomass partitioning, residue quality, cultivar improvement, and SCN (soy cyst nematode) resistance, as well as field pea breeding for Midwest adaptation. His contributions also extend to collaborative efforts implementing ground rovers and near-field abiotic stress testing to advance climate-resilient crops. Joscif regularly mentors students in drone operation and data-processing pipelines, fuelling innovation in plant breeding programs.