Faba bean and pea harvest index estimations using aerial-based multimodal data and machine learning algorithms.

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.

Microbial degradation of petroleum characteristic pollutants in hypersaline environment, emphasizing n-hexadecane and 2,4 di-tert-butylphenol.

In this paper, nine strains of salt-tolerant petroleum-degrading bacteria were applied to an biological aerated filter. Simulating the degradation of high-salinity petroleum wastewater with n-hexadecane and 2,4-ditert-butylphenol as the primary pollutants and analyzing the structure of the biofilm at various salt concentrations. According to the results, when the salinity was 4%, the COD removal efficiency reached 74.34%. Various halotolerant microorganisms have adapted to various salt concentrations. At a salinity of 3%, n-hexadecane exhibited the best degradation effect, with a rate of 83.21%. Shewanella, Acinetobacter, and Marinobacter were the predominant bacterial groups at the time. At 4% salinity, Acinetobacter and Pseudomonas were the predominant bacteria, and the average 2,4-ditert-butylphenol degradation rate was the highest at 63.02%. This study provided an experimental basis for further studying the biological treatment of high-salinity petroleum wastewater.

Large-Scale Heat-Tolerance Screening and Genetic Diversity of Pea (Pisum sativum L.) Germplasms.

Pea (Pisum sativum L.) is an important legume crop. However, the yield of pea is adversely affected by heat stress in China. In this study, heat-tolerant germplasms were screened and evaluated in the field under multi-conditions. The results showed that heat stress could significantly affect pea yield. On the basis of grain weight per plant, 257 heat-tolerant and 175 heat-sensitive accessions were obtained from the first year's screening, and 26 extremely heat-tolerant and 19 extremely heat-sensitive accessions were finally obtained in this study. Based on SNaPshot technology, two sets of SNP markers, including 46 neutral and 20 heat-tolerance-related markers, were used to evaluate the genetic diversity and population genetic structure of the 432 pea accessions obtained from the first year's screening. Genetic diversity analysis showed that the average polymorphic information content was lower using heat-tolerance-related markers than neutral markers because of the selective pressure under heat stress. In addition, population genetic structure analysis showed that neutral markers divided the 432 pea accessions into two subpopulations associated with sowing date type and geographical origin, while the heat-tolerance-related markers divided these germplasms into two subpopulations associated with heat tolerance and sowing date type. Overall, we present a comprehensive resource of heat-tolerant and heat-sensitive pea accessions through heat-tolerance screenings in multi-conditions, which could help genetic improvements of pea in the future.

Improved pea reference genome and pan-genome highlight genomic features and evolutionary characteristics.

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 that Pisum abyssinicum is a separate species different from P. fulvum and P. sativum within Pisum. 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 in P. abyssinicum and P. fulvum showed distinct functional enrichment, indicating the potential value of them as pea breeding resources in the future.

Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.).

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.

Genetic diversity of Lathyrus sp collected from different geographical regions.

BACKGROUND: The demand for grass peas (Lathyrus sativus L.) had increased as high nutritional safe food, but most of the accessions of South Asia and Africa had low grain harvest. Therefore, this study had been undertaken to collect grass pea germplasm for boosting yields and quality improvement. METHODS AND RESULTS: In this study, 400 accessions of grass pea from different geographical regions had characterized by using 56 Simple Sequences Repeat (SSRs) markers. In total 253 alleles were detected, the maximum and minimum polymorphic information content (PIC) indices were 0.70 and 0.34 found in markers G17922 and G18078, correspondingly. The germplasm was split into two main and one sub-group by cluster assay, by SSR assay, and three populations by model-based population structure analysis (Pop1, Pop2 and admixed). Neighbors joining tree assay showed the tested germplasm highly diverse in structure. Three-dimensional principal components analysis (PCA) and two dimensional principles coordinate analysis (PCoA) exhibited two main and one admixed group (P1, P2 and P1P2). In addition, FST population value of pairwise mean and analysis of molecular variance (AMOVA) showed high population structure across all pairs of populations on an average 0.1710 advocating all population structure categories varied significantly. The average predictable heterozygosity distant was 0.4472-0.4542 in same cluster for the individuals. CONCLUSION: Discovery from this study revealed SSR markers based polymorphic bands showed in the diversified grasspea germplasm which might be utilized as genetic resource of a breeding scheme and prospective uses for mapping analyses of recombinant inbred lines (RIL).

Development and application of the Faba_bean_130K targeted next-generation sequencing SNP genotyping platform based on transcriptome sequencing.

KEY MESSAGE: Large-scale faba bean transcriptome data are available, and the first genotyping platform based on liquid-phase probe targeted capture technology was developed for genetic and molecular breeding studies. Faba bean (Vicia faba L., 2n = 12) is an important food legume crop that is widely grown for multiple uses worldwide. However, no reference genome is currently available due to its very large genome size (approximately 13 Gb) and limited single nucleotide polymorphism (SNP) markers as well as highly efficient genotyping tools have been reported for faba bean. In this study, 16.7 billion clean reads were obtained from transcriptome libraries of flowers and leaves of 102 global faba bean accessions. A total of 243,120 unigenes were de novo assembled and functionally annotated. Moreover, a total of 1,579,411 SNPs were identified and further filtered according to a selection pipeline to develop a high-throughput, flexible, low-cost Faba_bean_130K targeted next-generation sequencing (TNGS) genotyping platform. A set of 69 Chinese faba bean accessions were genotyped with the TNGS genotyping platform, and the average mapping rate of captured reads to reference transcripts was 93.14%, of which 53.23% were located in the targeted regions. The TNGS genotyping results were validated by Sanger sequencing and the average consistency rate reached 93.6%. Comprehensive population genetic analysis was performed on the 69 Chinese faba bean accessions and identified four genetic subgroups correlated with the geographic distribution. This study provides valuable genomic resources and a reliable genotyping tool that could be implemented in genetic and molecular breeding studies to accelerate new cultivar development and improvement in faba bean.

Genetic diversity analysis for narrow-leafed lupin (Lupinus angustifolius L.) by SSR markers.

Narrow-leafed lupin (Lupinus angustifolius L.) is used as grain legumes, fodder for livestock and green manure in the world and has a great potential to be developed as a new crop in China. In this study, we assessed the genetic diversity among a set of 109 newly introduced accessions of narrow-leafed lupin using 76 genomic SSR markers. Data analysis suggested that the average gene diversity index and average polymorphism information content (PIC) were 0.4758 and 0.4328, respectively. The mean allele number per loci (Na) was 6.3816. The population structure analysis identified two subgroups based on delta K (ΔK) values. This result is in accordance with that of a PCA. The AMOVA analysis showed that most of molecular variance were within population. These results will be useful to guide the genetic improvement of the narrow-leafed lupin crop in China.