Planteome & BisQue: Automating image annotation with ontologies using deep-learning networks

Dimitrios Trigkakis, Sinisa Todorovic, Justin Preece, Austin Meier, Justin Elser, Pankaj Jaiswal, Kris Kvilekval, Dmitry Fedorov, BS Manjunath

Abstract


The field of computer vision has recently experienced tremendous progress due to advances in deep learning. This development holds particular promise in applications for plant research, due to a significant increase in the scale of image data harvesting and a strong field-driven interest in the automated processing of observable phenotypes and visible traits within agronomically important species.


Parallel developments have occurred in semantic computing; for example, new ontologies have been initiated to capture plant traits and disease indicators. When these ontologies are combined with existing segmentation capabilities, it is possible to conceptualize software applications that give researchers the ability to analyze large quantities of plant phenotype image data, and to auto-annotate that data with meaningful, computable semantic terminology.
 

[Link] [BibTex]
Dimitrios Trigkakis, Sinisa Todorovic, Justin Preece, Austin Meier, Justin Elser, Pankaj Jaiswal, Kris Kvilekval, Dmitry Fedorov, BS Manjunath,
“Planteome & BisQue: Automating image annotation with ontologies using deep-learning networks”,
2018.