2024 Award

Dr. Worasit Sangjan

Ph.D. in the Department of Biological Systems Engineering
Washington State University

Profile

Worasit Sangjan completed his Ph.D. in the Department of Biological Systems Engineering at Washington State University (WSU) under the supervision of Dr. Sindhuja Sankaran. His research integrates engineering and data science with an understanding of plant science to develop practical tools and advanced techniques for strengthening plant phenomics. In his phenomics application research, different scales of remote sensing platforms and sensors, ranging from spectroradiometer (hyperspectral), robotic system, and unmanned aerial vehicle (UAV) to satellite-based imaging, as well as his developed sensor—the Internet of Things (IoT) based sensor system based on Raspberry Pi and weather station sensor system, have been utilized. Worasit is currently working on using high-resolution temporal data from an IoT-based sensor system (AGIcam) associated with environmental data, including temporal UAV data, to enhance yield and heading date prediction accuracy in wheat breeding programs using machine and deep learning techniques.

Worasit’s research has been published in high-impact factor journals such as Computers and Electronics in Agriculture, International Journal of Remote Sensing, and Remote Sensing. He has received several awards, including the Boyd-Scott Graduate Research Award (Ph.D. category) from the American Society of Agricultural and Biological Engineers, the Dissertation Award from WSU, and the Outstanding Graduate Students Scholarship Award from the department, among others. He has mentored and trained several undergraduate and graduate students at WSU, including intern students from the USDA NIFA project on sensor applications for phenomics applications. Worasit’s dedication and commitment to his research and assisting others have an impact on research programs at WSU and around the world. Worasit has a BS in Agricultural Engineering, an MBA in Finance, and an ME in Agricultural Systems and Engineering.

2023 Award

Dr. Alper Adak

Ph.D. in the Department of Soil and Crop Sciences
Texas A&M University, USA

Alper Adak received his PhD from Texas A&M University and a BS from Akdeniz University, Turkey. His area of interest falls in researching high throughput phenomics (including unmanned aerial vehicles, i.e., drones) and genomics as well as their integration in crop plants. His aim is to better understand the interplays between phenomics and genomics and their interactions across diverse environments. His most recent publication shows how to (i) analyze  multi-time point phenomic data derived from an unmanned aerial system, (ii) use temporal phenomic data in prediction of complex traits for unknown genotypes in unobserved environments and (iii) associate temporal phenomic data with genomic data for dissecting the underlying genomic mechanism of complex traits across time points. He is currently continuing his research on integrating phenomic prediction and selection into plant breeding. His recent studies were published in journals in Crop Science Society of America (CSSA), Genetic Society of America (GSA), Multidisciplinary Digital Publishing Institute (MDPI) and Nature publishing group. He was awarded with Vice Chancellor's Awards in Excellence in 2022 for his research, 1st place in the Crop Science Society of America in the division of Crop Breeding and Genetics in 2019 for his project and earned a statistics certificate by Department of Statistics at Texas A&M University. Motivated colleagues in the research team is deeply embedded in all of his research motivation. Alper’s passion for research is bolstered by collaborations with motivated colleagues both in his own laboratory and throughout Texas A&M.

2022 Award

Dr. Rubi Quiñones

National Science Foundation (NSF) Research Trainee
Plant Vision Lab
Hydroinformatics and Integrated Hydroclimate Lab
Ph.D. from School of Computing, University of Nebraska-Lincoln, USA

Ms. Rubi Quiñones is a fifth-year Ph.D. Candidate from the School of Computing at the University of Nebraska-Lincoln. Rubi is part of a new generation of interdisciplinary researchers that are resilient and adaptive to the fast-paced, innovative research demands.

Ms. Quinones’ research focuses on developing artificial intelligence methods to co-segment complex, evolving objects leading to novel multi-dimensional phenotypes. She utilizes multi-feature datasets, high-throughput computing, and deep learning methods to advance plant phenotyping and computer vision topics. She is one of the first to integrate co-segmentation methods to plant phenotyping and has released her CosegPP dataset and her OSC-CO2 open-sourced code for other researchers.

Ms. Quiñones is an advocate for providing research opportunities in computer science to women and underrepresented minorities. She has been a data science mentor to several undergraduate students across many disciplines and has led and participated in various outreach programs. She is dedicated to increasing plant phenotyping’s visibility and impact by facilitating collaborations across disciplines in her future research.

2021 Award

Dr. Nathan Hein

Assistant Scientist
Crop Ecophysiology Lab (CEL)
Department of Agronomy | Kansas State University, USA
University webpage

Nathan Hein is a third-year Ph.D. student from the Department of Agronomy at Kansas State University (KSU). Mr. Hein’s research focuses in two areas: enhancing heat stress resilience and developing sensor-based tools to improve nitrogen use efficiency (NUE) in crops. Mr. Hein joined the Crop Ecophysiology Lab at KSU in 2018 and was immediately tasked with designing a field-based system to phenotype for high night-time temperature (HNT) stress impact on wheat. Mr. Hein was successful in designing, building, and publishing this methodology in Plant Methods. Mr. Hein then expanded and improved this methodology to create one of the world’s first mobile field-based systems to impose HNT stress on a large (320 accessions) winter wheat diversity panel. The new methodology, code to operate the Raspberry Pi system, and results were published in Scientific Reports. Mr. Hein plans to continue to investigate HNT impact on other row crops including maize and sorghum.

Mr. Hein is also working towards developing sensor-based decision making tools which will help producers improve NUE without negatively impacting yield and grain quality. Mr. Hein expertly crafted a data collection regime utilizing both ground- and aerial-based sensors which allowed for efficient repeatability and a high temporal dataset. Along with these experiments, Mr. Hein also authored a review article to identify bottlenecks in current high-throughput plant phenotyping for drought and heat stress resilience and proposed new methodologies and research directions to effectively implement sensor-based solutions to relieve these bottlenecks, which is available in the Journal of Experimental Botany.

Mr. Hein also acts as the Crop Ecophysiology Lab’s outreach coordinator and is actively involved with organizations which aim to develop interest in STEM careers in minority and female students. His ability to relate complex scientific principles via hands-on activities has exposed hundreds of students to STEM and agricultural science careers.

Mr. Nathan Hein is a third-year Ph.D. student from the Department of Agronomy at Kansas State University (KSU). Mr. Hein’s research focuses in two areas: enhancing heat stress resilience and developing sensor-based tools to improve nitrogen use efficiency (NUE) in crops. Mr. Hein joined the Crop Ecophysiology Lab at KSU in 2018 and was immediately tasked with designing a field-based system to phenotype for high night-time temperature (HNT) stress impact on wheat. Mr. Hein was successful in designing, building, and publishing this methodology in Plant Methods. Mr. Hein then expanded and improved this methodology to create one of the world’s first mobile field-based systems to impose HNT stress on a large (320 accessions) winter wheat diversity panel. The new methodology, code to operate the Raspberry Pi system, and results were published in Scientific Reports. Mr. Hein plans to continue to investigate HNT impact on other row crops including maize and sorghum.

Mr. Hein is also working towards developing sensor-based decision making tools which will help producers improve NUE without negatively impacting yield and grain quality. Mr. Hein expertly crafted a data collection regime utilizing both ground- and aerial-based sensors which allowed for efficient repeatability and a high temporal dataset. Along with these experiments, Mr. Hein also authored a review article to identify bottlenecks in current high-throughput plant phenotyping for drought and heat stress resilience and proposed new methodologies and research directions to effectively implement sensor-based solutions to relieve these bottlenecks, which is available in the Journal of Experimental Botany.

Mr. Hein also acts as the Crop Ecophysiology Lab’s outreach coordinator and is actively involved with organizations which aim to develop interest in STEM careers in minority and female students. His ability to relate complex scientific principles via hands-on activities has exposed hundreds of students to STEM and agricultural science careers.