The PhenoApps project will converge novel advances in image processing and machine vision to deliver transformative mobile applications through established breeder networks. User-friendly mobile apps for field-based high-throughput phenotyping that utilize novel image analysis algorithms will be built and deployed to model and extract plant phenotypes. To ensure both immediate, broad deployment and functionality on a diverse set of crops, breeder networks for cassava and wheat will be engaged, providing a diverse set of target plant phenotypes, environments, breeding programs and working cultures.
Dramatic increases in the speed and ability to collect precision phenotypic data are needed to decipher plant genomes and accelerate plant breeding. Over the past decade, the availability of genomic data has exploded while the methods to collect phenotypes have made minimal advancements. This has led to a dramatic imbalance in data sets connecting genotype to phenotype and highlighting phenotyping as the remaining major bottleneck in plant breeding programs. This project will advance the field of 3D graphics and modeling, data mining, and deep learning through integration of simultaneous ground truth phenotypic measurements and imaging with mobile technology.
Add graphical app workflow (prelim data → algorithms → beta users → feedback and changes → implementation → beta test → release)
|Seed size and shape||0||1KK|
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|Cassava mosaic disease||0||None|
|Cassava brown streak disease||0||None|