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The Plant Genome|January 8, 2025
Enhancing genomic-based forward prediction accuracy in wheat by integrating UAV-derived hyperspectral and environmental data with machine learning under heat-stressed environmentsJordan McBreen, Md Ali Babar, Diego Jarquin, et al.Frontiers in Plant Science|March 4, 2026
Optimizing biomass partitioning in wheat using UAV-based hyperspectral phenomic and genomic prediction: kernel-based and machine learning approachesSudip Kunwar, Md Ali Babar, Diego Jarquin, et al.Plant Phenomics (Washington, D.C.)|June 29, 2026
Deciphering the genetic basis of yield components in wheat by integrating hyperspectral-based phenomesSudip Kunwar, Md Ali Babar, Yiannis Ampatzidis, et al.TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|May 29, 2026
Uncovering the genetic architecture of biomass yield and related traits in Southern US oat germplasm using genome-wide association studySamuel A Adewale, Md Ali Babar, Naeem Khan, et al.Frontiers in Plant Science|April 6, 2026
Genomic prediction for stem rust resistance in the southern United States elite oat (<i>Avena sativa</i> L.) germplasmJanam Prabhat Acharya, Naeem Khan, Sudip Kunwar, et al.The Plant Genome|January 16, 2026
Effectiveness of low-density high-throughput marker platform and easy-to-measure traits for genomic prediction of biomass yield in oat (Avena sativa L.)Samuel A Adewale, Md Ali Babar, Diego Jarquin, et al.Pageof 1