Person: Pinto Espinosa, F.
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
Pinto Espinosa
First Name
F.
Name
Pinto Espinosa, F.
ORCID ID
0000-0003-3000-9084 8 results
Search Results
Now showing 1 - 8 of 8
- Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics(Oxford University Press, 2023) Togninalli, M.; Xu Wang; Kucera, T.; Shrestha, S.; Juliana, P.; Mondal, S.; Pinto Espinosa, F.; Velu, G.; Crespo Herrera, L.A.; Huerta-Espino, J.; Singh, R.P.; Borgwardt, K.; Poland, J.
Publication - Satellite imagery for high-throughput phenotyping in breeding plots(Frontiers, 2023) Pinto Espinosa, F.; Zaman-Allah, M.; Reynolds, M.P.; Schulthess, U.
Publication - Multimodal deep learning methods enhance genomic prediction of wheat breeding(2023) Montesinos-López, A.; Rivera-Amado, C.; Pinto Espinosa, F.; Piñera Chavez, F.J; González-Diéguez, D.; Reynolds, M.P.; Pérez-Rodríguez, P.; Huihui Li; Montesinos-Lopez, O.A.; Crossa, J.
Publication - Prediction of photosynthetic, biophysical, and biochemical traits in wheat canopies to reduce the phenotyping bottleneck(Frontiers, 2022) Robles-Zazueta, C.A.; Pinto Espinosa, F.; Molero, G.; Foulkes, J.; Reynolds, M.P.; Murchie, E.
Publication - Field-based remote sensing models predict radiation use efficiency in wheat(Oxford University Press, 2021) Robles-Zazueta, C.A.; Molero, G.; Pinto Espinosa, F.; Foulkes, J.; Reynolds, M.P.; Murchie, E.
Publication - Incorporating complex physiological traits into wheat breeding pipelines(CIMMYT, 2018) Molero, G.; Piñera Chavez, F.J; Rivera-Amado, C.; Gimeno, J.; Pinto Espinosa, F.; Sukumaran, S.; Saint Pierre, C.; Reynolds, M.P.
Publication - Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat(Springer, 2019) Juliana, P.; Montesinos-Lopez, O.A.; Crossa, J.; Mondal, S.; González Pérez, L.; Poland, J.; Huerta-Espino, J.; Crespo Herrera, L.A.; Velu, G.; Dreisigacker, S.; Shrestha, S.; Pérez-Rodríguez, P.; Pinto Espinosa, F.; Singh, R.P.Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center?s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress?resilience within years.
Publication - UAV-based imagery for phenotyping in breeding and physiological pre-breeding of wheat at CIMMYT(CIMMYT, [2016?]) Pinto Espinosa, F.; Reynolds, M.P.; Molero, G.; Mondal, S.
Publication