Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials
Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. G...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/6/1315 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849423813435981824 |
|---|---|
| author | Jordan McBreen Md Ali Babar Diego Jarquin Yiannis Ampatzidis Naeem Khan Sudip Kunwar Janam Prabhat Acharya Samuel Adewale Gina Brown-Guedira |
| author_facet | Jordan McBreen Md Ali Babar Diego Jarquin Yiannis Ampatzidis Naeem Khan Sudip Kunwar Janam Prabhat Acharya Samuel Adewale Gina Brown-Guedira |
| author_sort | Jordan McBreen |
| collection | DOAJ |
| description | Accurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles. |
| format | Article |
| id | doaj-art-4af474915b2845f48ea2501e5628d9dc |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-4af474915b2845f48ea2501e5628d9dc2025-08-20T03:30:28ZengMDPI AGAgronomy2073-43952025-05-01156131510.3390/agronomy15061315Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat TrialsJordan McBreen0Md Ali Babar1Diego Jarquin2Yiannis Ampatzidis3Naeem Khan4Sudip Kunwar5Janam Prabhat Acharya6Samuel Adewale7Gina Brown-Guedira8Department of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USADepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USADepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USAAgricultural and Biological Engineering Department, Southwest Florida Research and Education Center, University of Florida, IFAS, 2685 SR 29 North, Immokalee, FL 34142, USADepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USADepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USADepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USADepartment of Agronomy, University of Florida, 3105 McCarty Hall B, Gainesville, FL 32608, USAPlant Science Research, USDA-ARS SEA, Raleigh, NC 27695, USAAccurate grain yield (GY) prediction is essential in wheat breeding to enhance selection and accelerate breeding cycles. This study explored whether high-throughput phenotyping (HTP) data collected from small plot (SP) trials can effectively predict GY outcomes in later-stage big plot (BP) trials. Genomic (G) data were combined with hyperspectral (H) and multispectral + thermal (M) imaging across the 2022 and 2023 growing seasons at the Plant Science Research and Education Unit, Citra, Florida. A panel of 312 wheat genotypes was analyzed using GBLUP-based models, integrating G + H and G + M data from SP to predict BP yield. SP models demonstrated promising predictive ability, with G + H models achieving moderate within-year (0.43 to 0.51) and across-year (0.43) prediction accuracies, while G + M models reached 0.53 to 0.58 and 0.45, respectively. The Random Forest Regression (RFR) model produced an accuracy of 0.47 when M data from the 2022 SP, combined with G, was used to predict BP yield in 2023. Additionally, the top 25% specificity (coincide index) was evaluated, with models showing up to 47–51% within a year and 43–45% between years overlap in the highest predicted-yielding lines between SP and BP trials, further emphasizing the potential of SP data for early selection. These findings suggest that SP trials can provide meaningful predictions for BP yields, enabling earlier selection and faster breeding cycles.https://www.mdpi.com/2073-4395/15/6/1315grain yieldwheat breedinggenomic predictionhigh-throughput phenotypinghyperspectral imagingUAV phenotyping |
| spellingShingle | Jordan McBreen Md Ali Babar Diego Jarquin Yiannis Ampatzidis Naeem Khan Sudip Kunwar Janam Prabhat Acharya Samuel Adewale Gina Brown-Guedira Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials Agronomy grain yield wheat breeding genomic prediction high-throughput phenotyping hyperspectral imaging UAV phenotyping |
| title | Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials |
| title_full | Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials |
| title_fullStr | Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials |
| title_full_unstemmed | Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials |
| title_short | Leveraging Multi-Omics Data with Machine Learning to Predict Grain Yield in Small vs. Big Plot Wheat Trials |
| title_sort | leveraging multi omics data with machine learning to predict grain yield in small vs big plot wheat trials |
| topic | grain yield wheat breeding genomic prediction high-throughput phenotyping hyperspectral imaging UAV phenotyping |
| url | https://www.mdpi.com/2073-4395/15/6/1315 |
| work_keys_str_mv | AT jordanmcbreen leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT mdalibabar leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT diegojarquin leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT yiannisampatzidis leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT naeemkhan leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT sudipkunwar leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT janamprabhatacharya leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT samueladewale leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials AT ginabrownguedira leveragingmultiomicsdatawithmachinelearningtopredictgrainyieldinsmallvsbigplotwheattrials |