Complete and accurate reference genomes and annotations provide fundamental resources for functional genomics and crop breeding. Here we report a de novo assembly and annotation of a pea cultivar ZW6 with contig N50 of 8.98 Mb, which features a 243-fold increase in contig length and evident improvements in the continuity and quality of sequence in complex repeat regions compared with the existing one. Genome diversity of 118 cultivated and wild pea demonstrated thatPisum abyssinicumis a separate species different fromP. fulvumandP. sativumwithinPisum. Quantitative trait locus analyses uncovered two known Mendel’s genes related to stem length (Le/le) and seed shape (R/r) as well as some candidate genes for pod form studied by Mendel. A pan-genome of 116 pea accessions was constructed, and pan-genes preferred inP. abyssinicumandP. fulvumshowed distinct functional enrichment, indicating the potential value of them as pea breeding resources in the future.

Background: Faba bean is an important legume crop in the world. Plant height and yield are important traits for crop improvement. The traditional plant height and yield measurement are labor intensive and time consuming. Therefore, it is essential to estimate these two parameters rapidly and efficiently. The purpose of this study was to provide an alternative way to accurately identify and evaluate faba bean germplasm and breeding materials. Results: The results showed that 80% of the maximum plant height extracted from two-dimensional red–green–blue (2D-RGB) images had the best fitting degree with the ground measured values, with the coefficient of determination (R²), root-mean-square error (RMSE), and normalized root-mean-square error (NRMSE) were 0.9915, 1.4411 cm and 5.02%, respectively. In terms of yield estimation, support vector machines (SVM) showed the best performance (R² = 0.7238, RMSE = 823.54 kg ha⁻¹, NRMSE = 18.38%), followed by random forests (RF) and decision trees (DT). Conclusion: The results of this study indicated that it is feasible to monitor the plant height of faba bean during the whole growth period based on UAV imagery. Furthermore, the machine learning algorithms can estimate the yield of faba bean reasonably with the multiple time points data of plant height.

Background: Grasspea (Lathyrus sativus L., 2n = 14), a member of the family Leguminosae, holds great agronomic potential as grain and forage legume crop in the arid areas for its superb resilience to abiotic stresses such as drought, flood and salinity. The crop could not make much progress through conventional breeding in the past, and there are hardly any detailed molecular biology studies due to paucity of reliable molecular markers representative of the entire genome. Results: Using the 454 FLX Titanium pyrosequencing technique, 651,827 simple sequence repeat (SSR) loci were identified and 50,144 nonredundant primer pairs were successfully designed, of which 288 were randomly selected for validation among 23 L. sativus and one L. cicera accessions of diverse provenance. 74 were polymorphic, 70 monomorphic, and 144 with no PCR product. The number of observed alleles ranged from two to five, the observed heterozygosity from 0 to 0.9545, and Shannon’s information index ranged from 0.1013 to 1.0980, respectively. The dendrogram constructed by using unweighted pair group method with arithmetic mean (UPGMA) based on Nei's genetic distance, showed obvious distinctions and understandable relationships among the 24 accessions. Conclusions: The large number of SSR primer pairs developed in this study would make a significant contribution to genomics enabled improvement of grasspea.

Early and high-throughput estimations of the crop harvest index (HI) are essential for crop breeding and field management in precision agriculture; however, traditional methods for measuring HI are time-consuming and labor-intensive. The development of unmanned aerial vehicles (UAVs) with onboard sensors offers an alternative strategy for crop HI research. In this study, we explored the potential of using low-cost, UAV-based multimodal data for HI estimation using red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors at 4 growth stages to estimate faba bean (Vicia faba L.) and pea (Pisum sativum L.) HI values within the framework of ensemble learning. The average estimates of RGB (faba bean: coefficient of determination [R²] = 0.49, normalized root-mean-square error [NRMSE] = 15.78%; pea: R² = 0.46, NRMSE = 20.08%) and MS (faba bean: R² = 0.50, NRMSE = 15.16%; pea: R² = 0.46, NRMSE = 19.43%) were superior to those of TIR (faba bean: R² = 0.37, NRMSE = 16.47%; pea: R² = 0.38, NRMSE = 19.71%), and the fusion of multisensor data exhibited a higher estimation accuracy than those obtained using each sensor individually. Ensemble Bayesian model averaging provided the most accurate estimations (faba bean: R² = 0.64, NRMSE = 13.76%; pea: R² = 0.74, NRMSE = 15.20%) for whole growth stage, and the estimation accuracy improved with advancing growth stage. These results indicate that the combination of low-cost, UAV-based multimodal data and machine learning algorithms can be used to estimate crop HI reliably, therefore highlighting a promising strategy and providing valuable insights for high spatial precision in agriculture, which can help breeders make early and efficient decisions.