More Like this

1. Genomic and phenomic prediction for soybean seed yield, protein, and oil

   https://doi.org/10.1002/tpg2.70002  

   Van_der_Laan, Liza; Parmley, Kyle; Saadati, Mojdeh; Pacin, Hernan_Torres; Panthulugiri, Srikanth; Sarkar, Soumik; Ganapathysubramanian, Baskar; Lorenz, Aaron; Singh, Asheesh_K (February 2025, The Plant Genome)

   Abstract Developments in genomics and phenomics have provided valuable tools for use in cultivar development. Genomic prediction (GP) has been used in commercial soybean [Glycine maxL. (Merr.)] breeding programs to predict grain yield and seed composition traits. Phenomic prediction (PP) is a rapidly developing field that holds the potential to be used for the selection of genotypes early in the growing season. The objectives of this study were to compare the performance of GP and PP for predicting soybean seed yield, protein, and oil. We additionally conducted genome‐wide association studies (GWAS) to identify significant single‐nucleotide polymorphisms (SNPs) associated with the traits of interest. The GWAS panel of 292 diverse accessions was grown in six environments in replicated trials. Spectral data were collected at two time points during the growing season. A genomic best linear unbiased prediction (GBLUP) model was trained on 269 accessions, while three separate machine learning (ML) models were trained on vegetation indices (VIs) and canopy traits. We observed that PP had a higher correlation coefficient than GP for seed yield, while GP had higher correlation coefficients for seed protein and oil contents. VIs with high feature importance were used as covariates in a new GBLUP model, and a new random forest model was trained with the inclusion of selected SNPs. These models did not outperform the original GP and PP models. These results show the capability of using ML for in‐season predictions for specific traits in soybean breeding and provide insights on PP and GP inclusions in breeding programs. 

   more » « less
2. Robust soybean seed yield estimation using high-throughput ground robot videos

   https://doi.org/10.3389/fpls.2025.1554193  

   Feng, Jiale; Blair, Samuel W; Ayanlade, Timilehin T; Balu, Aditya; Ganapathysubramanian, Baskar; Singh, Arti; Sarkar, Soumik; Singh, Asheesh K (March 2025, Frontiers in Plant Science)

   We present a novel method for soybean [Glycine max(L.) Merr.] yield estimation leveraging high-throughput seed counting via computer vision and deep learning techniques. Traditional methods for collecting yield data are labor-intensive, costly, and prone to equipment failures at critical data collection times and require transportation of equipment across field sites. Computer vision, the field of teaching computers to interpret visual data, allows us to extract detailed yield information directly from images. By treating it as a computer vision task, we report a more efficient alternative, employing a ground robot equipped with fisheye cameras to capture comprehensive videos of soybean plots from which images are extracted in a variety of development programs. These images are processed through the P2PNet-Yield model, a deep learning framework, where we combined a feature extraction module (the backbone of the P2PNet-Soy) and a yield regression module to estimate seed yields of soybean plots. Our results are built on 2 years of yield testing plot data—8,500 plots in 2021 and 650 plots in 2023. With these datasets, our approach incorporates several innovations to further improve the accuracy and generalizability of the seed counting and yield estimation architecture, such as the fisheye image correction and data augmentation with random sensor effects. The P2PNet-Yield model achieved a genotype ranking accuracy score of up to 83%. It demonstrates up to a 32% reduction in time to collect yield data as well as costs associated with traditional yield estimation, offering a scalable solution for breeding programs and agricultural productivity enhancement. 

   more » « less
3. Simple Mechanisms of Plant Reproductive Benefits Yield Different Dynamics in Pollination and Seed Dispersal Mutualisms

   https://doi.org/10.1086/720204  

   Hale, Kayla R.; Maes, Daniel P.; Valdovinos, Fernanda S. (August 2022, The American Naturalist)
4. Enhancement of biogas yield during anaerobic digestion of Jatropha curcas seed by pretreatment and co-digestion with mango peels

   https://doi.org/10.1007/s13399-020-01064-7  

   Haq, A.; Khan, A.; Haji, K.; Khan, S.; Shah, A.A.; Hasan, F.; Ahmed, Safia; de los Reyes, F.L.; Badshah, M. (May 2022, Biomass Conversion and Biorefinery)
5. Enhancing seed yield and nitrogen use efficiency of Brassica napus L. under low nitrogen by overexpression of G-proteins from Arabidopsis

   https://doi.org/10.1093/jxb/eraf130  

   Xie, Yuyu; Nan, Yunyou; Atif, Ayub; Shi, Derong; Tian, Hui; Hui, Jing; Zhang, Yanfeng; Jones, Alan M; Gao, Yajun (March 2025, Journal of Experimental Botany)

   Beckles, Diane (Ed.)

   Abstract Heterotrimeric G-proteins, composed of Gα, Gβ, and Gγ subunits, are involved in the regulation of multiple signaling pathways in plants. OsDEP1 (a Gγ subunit) of rice and TaNBP1 (a Gβ subunit) of wheat are homologs of Arabidopsis AGG3 and AGB1, respectively, which are regulators of grain size and also involved in nitrogen responses. However, the function of Arabidopsis G-proteins in nitrogen utilization under different nitrogen conditions has not been fully investigated. In this study, to evaluate the role of Arabidopsis G-proteins in yield and nitrogen use efficiency (NUE), overexpression transgenic lines AtGPA1, AtAGB1 together with AtAGG1 (AGB1-AGG1), AtAGB1 together with AtAGG2 (AGB1-AGG2), and AtAGB1 together with AtAGG3 (AGB1-AGG3) were created in Brassica napus ‘K407’. Analysis of multiple transgenic B. napus lines showed that overexpression of GPA1, AGB1-AGG1, AGB1-AGG2, or AGB1-AGG3 led to increased biomass of seedling plants, including a well-developed root system, and increased nitrogen uptake under low and high nitrogen conditions. The activity of glutamine synthetase, a key nitrogen assimilating enzyme, and the expression levels of genes that are involved in nitrogen uptake and assimilation were significantly increased in overexpression plants under the low nitrogen condition. These properties enabled overexpression plants to increase the number of seeds per silique by 12–27% only under the low nitrogen condition, effectively improving yield per plant by 9–69% and NUE by 7–49%. These results reveal roles of G-proteins in regulating seed traits and NUE, and provide a strategy that can substantially improve crop yield and NUE. 

   more » « less