BMC Bioinformatics. 2026 Jan 21. doi: 10.1186/s12859-025-06352-5. Online ahead of print.
ABSTRACT
BACKGROUND: Accurate acquisition of phenotypic data is critical for cataloguing and utilising genetic variation in cultivated crops, landraces, and their wild relatives. The collection of phenotypic data using handwritten notes often introduces errors which can and should be avoided. Electronic data collection is crucial for ensuring error prevention and data standardisation and thus ensuring high-quality, reliable data.
IMPLEMENTATION: This paper describes the development of GridScore NEXT, a new plant phenotyping application that significantly advances the state of the art for collecting field trial data in plant genetics, pre-breeding and crop improvement research. Building on its predecessor, GridScore, the development of GridScore NEXT was driven by real life, in the field interactions with expert user groups across a number of crops. This iterative design methodology allowed the development and testing of new features. Collaborators from the ‘Biodiversity for Opportunities, Livelihoods and Development’ (BOLD) project, focusing on crops including rice, grasspea, and alfalfa, along with barley, potato, vegetable and blueberry teams, provided invaluable insights through training sessions and interviews and in the field use of the application.
RESULTS: Key improvements to GridScore NEXT include enhanced data collection tools, supporting individual plant phenotyping within plots and enabling new data types such as GPS coordinates and image traits. GridScore NEXT provides customisable user defined validation rules to help prevent errors and incorporates barcode scanning for accurate, efficient data capture. The application offers an increased toolbox of data visualizations over its predecessor including heatmaps and statistical box plots, which aid in identifying potential data issues and understanding trial performance in the field. GridScore NEXT is cross-platform and can operate without an internet connection, making it ideal for field use in remote areas. Its adoption has led to standardisation of methods, significant error reduction, and the timely sharing of data, enabling quicker decision-making in pre-breeding and characterisation experiments. GridScore NEXT is available under an open-source (Apache 2.0) licence and freely available to all with no restrictions. It offers self-hosting options for enhanced data security and privacy. GridScore NEXT shows broad applicability across a diverse range of not only plant phenotyping experiments, but any experiment that requires the collection of accurate data.
PMID:41566195 | DOI:10.1186/s12859-025-06352-5
