Soybean (Glycine max) root nodules, formed through symbiosis with nitrogen-fixing rhizobia, are essential for biological nitrogen fixation. While quantifying key nodulation traits, nodule number and weight, is critical for assessing symbiotic efficiency and yield potential, current methods are destructive and labor-intensive, unsuitable for longitudinal monitoring and high-throughput phenotyping. Here, we established hyperspectral leaf reflectance as a non-destructive, high-resolution tool capable of monitoring root nodule development. Using Partial Least Squares Regression models, we connected spectral data with nodule metrics from 528 unique soybean plants across 18 genotypes, inoculated with different rhizobium strains, and under different abiotic stresses. These models achieved high accuracy for predicting nodule number (R² = 0.75, nRMSE = 6.02%) and moderate accuracy for nodule weight (R² = 0.53, nRMSE = 12.38%). Crucially, spectral analyses revealed distinct hyperspectral signatures sensitive to nodule traits. While different rhizobium strains induced comparable changes in both nodule traits, and therefore produced highly overlapped spectral domains, diagnostically distinct spectral patterns were generated under drought versus salt stress, with the former suppressing nodulation more significantly than the latter. Furthermore, we demonstrated the effectiveness of our models for real-time in-situ monitoring of nodule development for individual plants. Spectral-nodule trait covariation analyses further revealed leaf signatures correlated with nodule traits primarily through systemic physiological coupling governed by carbon-nitrogen exchange dynamics and plant water status. This study showcased hyperspectral sensing as a transformative methodology, enabling the unprecedented non-destructive quantification of nodulation dynamics, revealing novel physiological insights into plant-microbe-environment interactions, facilitating breeding and management strategies for sustainable soybean production.

Soybeans specially the widely planted cultivars have been dramatically improved in agronomic performance and is well adapted to local planting environments after long-time domestication and breeding. Uncovering the unique genomic features of popular cultivars will help to understand how soybean genomes have been modified through breeding. We re-sequenced 134 soybean cultivars that were released and most widely planted over the last century in China. Phylogenetic analyses established that these cultivars comprise two geographically distinct sub-populations: Northeast China (NE) versus the Huang-Huai-Hai River Valley and South China (HS). A total of 309 selective regions were identified as being impacted by geographical origins. The HS sub-population exhibited higher genetic diversity and linkage disequilibrium decayed more rapidly compared to the NE sub-population. To study the association between phenotypic differences and geographical origins, we recorded the vegetative period under different growing conditions for two years, and found that clustering based on the phenotypic data was closely correlated with cultivar geographical origin. By iteratively calculating accumulated genetic diversity, we established a platform panel of cultivars and have proposed a novel breeding strategy named “Potalaization” for selecting and utilizing the platform cultivars that represent the most genetically diversity and the highest available agronomic performance as the “plateau” for accumulating elite loci and traits, breeding novel widely adapted cultivars, and upgrading breeding technology. In addition to providing new genomic information for the soybean research community, the “Potalaization” strategy that we devised will also be practical for integrating the conventional and molecular breeding programs of crops in the post-genomic era.