Rapid industrialization, urbanization, and mine tailings runoff are the main sources of heavy metal contamination of agricultural land, which has become one of the major constraints to crop growth and productivity. Finding appropriate solutions to protect plants and agricultural land from heavy metal pollution/harmful effects is important for sustainable development. Phytoremediation and plant growth-promoting rhizobacteria (PGPR) are promising methods for this purpose, which both heavily rely on an appropriate understanding of the anatomical structure of plants. Specialized anatomical features, such as those of epidermis and endodermis and changes in the root vascular tissue, are often associated with heavy metal tolerance in legumes. This review emphasizes the uptake and transport of heavy metals by legume plants that can be used to enhance soil detoxification by phytoremediation processes. Moreover, the review also focuses on the role of rhizospheric organisms in the facilitation of heavy metal uptake, the various mechanisms of enhancing the availability of heavy metals in the rhizosphere, the genetic diversity, and the microbial genera involved in these processes. The information presented here can be exploited for improving the growth and productivity of legume plants in metal-prone soils.

Illuminating the phenotype–genotype black box under complex traits is an ambitious goal for researchers. The generation of temporally or spatially phenotypic data today has far outpaced its interpretation, due to their highly dynamic nature depending on the environment and developmental stages. Here, we propose an integrated enviro-pheno-geno functional approach to pinpoint the major challenges of decomposing physiological traits. The strategy first features high-throughput functional physiological phenotyping (FPP) to efficiently acquire phenotypic and environmental data. It then features functional mapping (FM) and the extended systems mapping (SM) to tackle trait dynamics. FM, by modeling traits as continuous functions, can increase the power and efficiency in dissecting the spatiotemporal effects of QTLs. SM could enable reconstruction of a genotype–phenotype map from developmental pathways. We present a recent case study that combines FPP and SM to dissect complex physiological traits. This integrated approach will be an important engine to drive the translation of phenomic big data into genetic gain.

White-fruited bitter melon is a rare species of Momordica charantia L., valued for its pearl-like white pericarp. However, limited research is available regarding its supporting cultivation techniques and fruit quality. Two-season field trials were performed using ‘Kuzhongle’ as scion to evaluate the applicability of luffa rootstocks and seasonal variation in rootstock-mediated effects. Three luffa inbred lines (SS07, SS11 and SS26) were screened in spring, and the superior SS26 was further compared with the commercial rootstock ‘Yinzhen No.1’ in autumn. All rootstocks increased stem diameters, shortened internodes and promoted female flower formation in spring, but their effects on fruit quality varied among genotypes. SS26 exhibited the highest survival rate and significantly enhanced soluble solid content, water content, crude protein content and vitamin C content, while reducing crude fiber content and titratable acidity content. Seasonal comparison revealed consistent quality-modulating effects of SS26 across seasons, except for crude fiber, with a comprehensive performance comparable to the commercial rootstock. Overall, luffa rootstocks consistently modulate vegetative and flowering traits within a season, whereas their impacts on fruit quality show genotypic differences. SS26 exhibits stable and superior grafting performance, providing important material support for bitter melon rootstock breeding research.

White-fruited bitter melon (Momordica charantia L.) is a rare species of Momordica charantia L, valued for its pearl-like white pericarp. However, limited research is available regarding its supporting cultivation techniques and fruit quality. Two-season field trials were performed using ‘Kuzhongle’ as scion to evaluate the applicability of luffa rootstocks and seasonal variation in rootstock-mediated effects. Three luffa inbred lines (SS07, SS11, SS26) were screened in spring, and the superior SS26 was further compared with the commercial rootstock ’Yinzhen No.1’ in autumn. All rootstocks thickened stems, shortened internodes and promoted female flower formation in spring, with genotypically divergent influences on fruit quality. SS26 exhibited the highest survival rate and significantly enhanced soluble solid content, water content, crude protein content and vitamin C content, while reducing crude fiber content and total acid content. Seasonal comparison revealed consistent quality-modulating effects of SS26 across seasons, except for crude fiber, with a comprehensive performance comparable to the commercial rootstock. Overall, luffa rootstocks consistently modulate vegetative and flowering traits within a season, whereas their impacts on fruit quality show genotypic differences. SS26 exhibits stable and superior grafting performance, providing important material support for bitter gourd rootstock breeding research.

Multi-view 3D reconstruction has been widely applied in plant phenotyping, but the complex canopy structure of rice plants poses significant challenges for reconstruction accuracy, completene7ss, and efficiency. In this study, we developed an optimized workflow for 3D reconstruction of rice using the self-developed Metatlas V1 multi-view imaging platform combined with the COLMAP + OpenMVS pipeline. A CBAM-UNet-based image segmentation model was used to extract plant regions from complex backgrounds, outperforming thresholding, U-Net, and U-Net++, and increasing the number of reconstructed points. Camera configuration optimization was performed by systematically combining equidistant and greedy strategies, resulting in an optimal six-camera setup (#2, #3, #4, #6, #7, and #8, corresponding to −30°, −15°, 0°, +30°, +45°, and +60° relative to the horizontal viewpoint), which provided complementary views of the canopy inner structure and stem base. This configuration reduced the average nearest-neighbor distance to 0.037–0.066 cm across different varieties and growth stages compared with the full 11-camera setup, retained 73–76% of the points, and decreased reconstruction duration by approximately 70–80%. The strong agreement between point-cloud-derived and manually measured plant height and canopy width ( R ² = 0.989 and 0.946, respectively) supported the accuracy of the point-cloud-derived phenotypic measurements. Overall, integrating image segmentation with camera-configuration optimization offers an accurate and efficient solution for high-throughput 3D phenotyping of rice using Metatlas V1, balancing reconstruction accuracy, point-cloud completeness, and computational efficiency, and may provide a methodological reference for camera-configuration design in other rotational multi-view phenotyping platforms.