Latest paper from our group with cross-disciplinary collaborators was published in Plant Methods Journal. This work is another example of leveraging cross-discipline expertise to tackle big problems and also for the development of phenomics capabilities.
In this paper we provide solutions to efficient and automatic phenotyping of traits across large populations, with an ability to sample multiple environments and growing replicated trials. We present a promising approach to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) was carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field. We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications. Our group continues to advance phenomics and its integration with genomics.
Authors include students, PDFs and faculty members in engineering and agronomy at ISU.
Congratulation to all authors for their contributions, and special thanks to the funding agencies for enabling this inter-disciplinary work!!
Full paper can be read at: https://plantmethods.biomedcentral.com/articles/10.1186/s13007-017-0173-7