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RootNet: A deep learning framework for automated tomato radicle segmentation and length measurement
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DOI：10.1016/j.scienta.2026.114949.png)
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Abstract
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• A high-throughput method for measuring radicle length during tomato seed germination. • Designed a seed germination phenotype acquisition system for non-destructive acquisition of time-series images during seed germination. • Designed MambaNextBlock(MNB)module to enhance boundary features, and introduced Atrous Spatial Pyramid Pooling(ASPP) module to improve multi-scale modeling capability. • Applied to assess the effects of drought, salinity, Streptomyces albidoflavus (HL4), and Streptomyces virginiae (GZ2) on tomato radicle growth.
Keywords:
Seed germination
Deep learning
Semantic segmentation
Radicle length measurement
Non-destructive phenotyping
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