Food and nutritional security is the grand challenge for the coming decades. The global population is expected to increase to over 9 billion and food demand will grow by more than 50%. To address this challenge, novel advancements to leverage genomic information and expedite the improvement of plant varieties are needed. While genomic information has become inexpensive and readily available, the complementary phenotypes needed to understand the function of plant genomes and make selections in breeding programs has had insufficient development, particularly for phenotypes collected from field trials in breeding programs.
PhenoApps converge novel advances in image processing, inexpensive sensors, and machine learning to deliver transformative mobile applications through established breeder networks. Thousands of scientists utilize different tools produced by the PhenoApps team in breeding programs around the world. Our innovative tools have been rapidly deployed through readily available and highly penetrant mobile technology which has allowed rapid dissemination and broad usability.
The PhenoApps team has worked directly with breeders of disparate crops that collectively collect millions of data points each season. Harnessing the diversity of crops, phenotypes, environments, breeding programs, and cultures, we have been able to develop and deliver broadly adaptable tools that can be utilized for genetics research and breeding. The ability to process and analyze complex phenotypes will provide the foundation for increasing genetic gain that will result in improved productivity, food security, nutrition, and income of smallholder farmers and their families in developing countries.
The PhenoApps project is managed by the Rife Lab at Clemson University.