A deep learning-integrated micro-CT imageanalysis pipeline for quantifying rice lodgingresistance-related traits

Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architec-
ture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm
structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lod-
ging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and
labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning
(SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morpho-
logical traits and lodging resistance-related traits. When manual and automatic measurements were
compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm,
and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R2
values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and
tillering stages, and the R2 values were 0.722 and 0.544, respectively. The modeling results indicated that this
method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also
evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and
found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and
culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT
image analysis pipeline to accurately quantify the phenotypic traits of rice culms in 4.6 min per plant; this
pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.