PhenoCitrus: An automated platform to phenotyping morphological traits of citrus fruit

Abstract

Citrus breeding critically relies on precise phenotyping, yet existing RGB/3D phenotyping methods lack standardized workflows balancing affordability, accuracy, and efficiency. An integrated hardware–software pipeline is introduced to address this gap: (1) a custom imaging device for standardized top/side-view capture of the sample fruit, and (2) optimized computer vision algorithms using enhanced 3D Gaussian Splatting (3DGS) to extract 3D structural traits (surface area, volume) and 2D algorithms (e.g., Unet++ and YOLO variants) for 2D trait (width, length, oil cell number, peel/pulp color, peel thickness, and segment number) quantification. Our approach achieved average Pearson correlations above 0.9 between algorithmic and manual measurements across five key traits, confirming measurement precision. The resulting phenotypic profiles revealed biologically significant inter-trait correlations to inform trait-oriented selection decisions and the refinement of breeding strategies. Finally, we operationalized this workflow through user-friendly software, delivering an end-to-end solution that enables high-throughput, low-cost citrus phenotyping with comparatively high accuracy for accelerated breeding applications. Code is available at https://github.com/liangzhao2000/PhenoCitrus . • Low-cost phenotyping device: Dual cameras enable comprehensive fruit imaging. • Precise trait extraction: Multiple deep learning methods extract fruit traits. • Phenotypic profiling: Statistical analysis supports trait-based variety selection.

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