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 ph