GAN-based image prediction of maize growth across varieties and developmental stages.

Background: Plant growth prediction assists physiologists and botanists in analyzing future development trends, thereby shortening experimental cycles and reducing costs. Traditional growth prediction methods mainly focused on phenotypic traits instead of images, which leads to limited visual interpretability.

Results: This article proposed a visualized growth prediction method based on an improved Pix2PixHD network, incorporating spatial attention mechanisms, an improved loss function, and a modified dropout strategy to enhance prediction accuracy and visual fidelity.

Multi-omics assists genomic prediction of maize yield with machine learning approaches.

Unlabelled: With the improvement of high-throughput technologies in recent years, large multi-dimensional plant omics data have been produced, and big-data-driven yield prediction research has received increasing attention. Machine learning offers promising computational and analytical solutions to interpret the biological meaning of large amounts of data in crops. In this study, we utilized multi-omics datasets from 156 maize recombinant inbred lines, containing 2496 single nucleotide polymorphisms (SNPs), 46 image traits (i-traits) from 16 developmental stages obtained through an automatic phenotyping platform, and 133 primary metabolites.

Three types of genes underlying the Gametophyte factor1 locus cause unilateral cross incompatibility in maize.

Unilateral cross incompatibility (UCI) occurs between popcorn and dent corn, and represents a critical step towards speciation. It has been reported that ZmGa1P, encoding a pectin methylesterase (PME), is a male determinant of the Ga1 locus. However, the female determinant and the genetic relationship between male and female determinants at this locus are unclear.