POSE PHASE I: Establishing an Open-Source Ecosystem for Plant Phenotyping

Feeding a growing global population requires crop varieties that can withstand drought, heat, pests, and extreme weather while maintaining high yields. Developing these resilient varieties requires the phenotyping of thousands of candidate plants under real-world conditions. Despite significant improvements to sensors, imaging, and computing, in-field phenotyping remains a major bottleneck for modern plant breeding. This project focuses on a collection of open-source mobile applications called PhenoApps that leverage advances in consumer electronics, image processing, and machine learning to improve digital data collection for plant breeding and genetics research. The PhenoApps suite allows breeders to capture high-quality data at scale using apps that target specific breeding activities, including field phenotyping, tissue sampling, and controlled crossing. Opening up wide access to the tools needed to breed crops more efficiently will accelerate the delivery of improved varieties and result in a more competitive U.S. agricultural sector with strengthened public plant breeding capacity. Breeders around the world have integrated PhenoApps into their research programs and are actively using these tools to address the global challenge of food security. This project will sustain and expand this impact by establishing the organizational, technical, and community foundations required to transition PhenoApps into an open-source ecosystem that serves as the default field-based phenotypic data collection platform. Specifically, the team will 1) characterize the existing user base via download metrics, targeted surveys, and interviews to identify unmet needs, barriers to adoption, and opportunities for new contributors; 2) establish a governance framework to explore long-term sustainability mechanisms; and 3) recruit complementary contributors with expertise spanning coding, plant breeding, and community management. Transitioning PhenoApps into a community-governed, open-source ecosystem with broad participation from developers, breeders, students, and technicians will create a unified, extensible phenotyping platform that can be harnessed to accelerate genetic gain and further develop improved crop varieties. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2550133 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Trevor Rife | Institution: Clemson University, CLEMSON, SC | Award Amount: $299,866 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2550133 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2550133.html