Improving field phenotyping accuracy in rice: a comparative study of smartphone image analysis and manual measurement

Rice (Oryza sativa L.) production is threatened by bacterial leaf blight (BLB), particularly from the virulent K3a strain. Reliableand efficient phenotyping is essential for breeding resistant cultivars, but traditional manual methods are time-consuming and prone to subjectivity. This study assessed the accuracy of smartphone-based image analysis for measuring lesion length (LL) and lesion ratio (LR) in BLB-infectedrice. A total of 102 recombinant inbred lines (RILs) derived from Chamdongjin and Sindongjin were evaluated using both manual methods and imageanalysis. Images were captured in the field using a smartphone (12MP, 4000×3000 pixels), without any auxiliary equipment. The images were analyzedusing ImageJ and Python-based tools and compared with manual measurements. Image-based data showed strong correlation with manual results(r = 0.84–1.00), and principal component analysis (PCA) explained 94.14% of the variance, effectively distinguishing resistant and susceptible lines.Statistically significant differences were observed between groups (p < 0.01), confirming reliability. Images taken at 3x magnification showed highaccuracy (r = 0.88–0.92), while those at 1x magnification showed reduced accuracy (r = 0.76–0.79), likely due to the inclusion of surrounding plants—highlighting the importance of proper field design and image framing. This study suggests that smartphone-acquired images, even withoutauxiliary equipment, may serve as a feasible and sufficiently accurate source for image-based phenotyping. This accessible approach has the potential tosupport rapid, low-cost data collection in field settings, which could contribute to breeding and disease resistance research. Smartphone-based image measurements were strongly correlated with conventional measurements of rice bacterial leaf blight symptoms. Images captured at3×magnification showed stronger correlations with reference measurements than images captured at 1×magnification. Lesion ratio showed greaterconsistency across ImageJ and Python analyses than lesion length. Smartphone-based field imaging distinguished resistant and susceptible rice lineswithout additional imaging equipment.