Harnessing the power of machine learning for crop improvement and sustainable production
SMH Khatibi, J Ali - Frontiers in Plant Science, 2024 - frontiersin.org
Crop improvement and production domains encounter large amounts of expanding data
with multi-layer complexity that forces researchers to use machine-learning approaches to …
with multi-layer complexity that forces researchers to use machine-learning approaches to …
[HTML][HTML] Integrating deep learning for phenomic and genomic predictive modeling of Eucalyptus trees
F Mora-Poblete, D Mieres-Castro… - Industrial Crops and …, 2024 - Elsevier
Genomic and phenomic prediction (GP and PP, respectively) are innovative methods that
allow plant breeders to increase the productivity of crops. Traditional methods for conducting …
allow plant breeders to increase the productivity of crops. Traditional methods for conducting …
Dual-model GWAS Analysis and Genomic Selection of Maize Flowering Time-Related Traits
Z Fan, S Lin, J Jiang, Y Zeng, Y Meng, J Ren, P Wu - Genes, 2024 - mdpi.com
An appropriate flowering period is an important selection criterion in maize breeding. It plays
a crucial role in the ecological adaptability of maize varieties. To explore the genetic basis of …
a crucial role in the ecological adaptability of maize varieties. To explore the genetic basis of …
[HTML][HTML] FTGD: a machine learning method for flowering-time gene prediction
J Zhang, S He, W Wang, F Chen, Z Li - Tropical Plants, 2023 - maxapress.com
The timing of flowering significantly affects plant reproduction and crop yield, making it
important to detect flowering-time associated genes. In this study, we retrieved 628 flowering …
important to detect flowering-time associated genes. In this study, we retrieved 628 flowering …