
PhenoImage : An open‐source graphical user interface for plant image analysis
Author(s) -
Zhu Feiyu,
Saluja Manny,
Dharni Jaspinder Singh,
Paul Puneet,
Sattler Scott E.,
Staswick Paul,
Walia Harkamal,
Yu Hongfeng
Publication year - 2021
Publication title -
the plant phenome journal
Language(s) - English
Resource type - Journals
ISSN - 2578-2703
DOI - 10.1002/ppj2.20015
Subject(s) - graphical user interface , computer science , usability , scalability , user interface , open source , flexibility (engineering) , rgb color model , interface (matter) , software , workflow , image processing , visualization , human–computer interaction , data mining , database , artificial intelligence , operating system , image (mathematics) , bubble , maximum bubble pressure method , statistics , mathematics
High‐throughput genotyping coupled with molecular breeding approaches have dramatically accelerated crop improvement programs. More recently, improved plant phenotyping methods have led to a shift from manual measurements to automated platforms with increased scalability and resolution. Considerable effort has also gone into developing large‐scale downstream processing of the imaging datasets derived from high‐throughput phenotyping (HTP) platforms. However, most available tools require some programming skills. We developed PhenoImage , an open‐source graphical user interface (GUI) based cross‐platform solution for HTP image processing intending to make image analysis accessible to users with either little or no programming skills. The open‐source nature provides the possibility to extend its usability to meet user‐specific requirements. The availability of multiple functions and filtering parameters provides flexibility to analyze images from a wide variety of plant species and platforms. PhenoImage can be run on a personal computer as well as on high‐performance computing clusters. To test the efficacy of the application, we analyzed the LemnaTec Imaging system derived red, green, and blue (RGB) color intensity and plant pigmentation‐based fluorescence shoot images from two plant species: sorghum [ Sorghum bicolor (L.) Moench] and wheat ( Triticum aestivum L.) differing in their physical attributes. In the study, we discuss the development, implementation, and working of the PhenoImage .