Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction
methods have been proposed including the standard additive genetic effect model for which …

Technological developments have revolutionized measurements on plant genotypes and
phenotypes, leading to routine production of large, complex data sets. This has led to …

In this chapter, we go through the fundamentals of artificial neural networks and deep
learning methods. We describe the inspiration for artificial neural networks and how the …

The overfitting phenomenon happens when a statistical machine learning model learns very
well about the noise as well as the signal that is present in the training data. On the other …

Continued increases in genetic gain demonstrate the success of established public and
private plant breeding programs. Nevertheless, in the last two decades, a growing body of …

Breeding crops for high yield and superior adaptability to new and variable climates is
imperative to ensure continued food security, biomass production, and ecosystem services …

Warming and drought are reducing global crop production with a potential to substantially
worsen global malnutrition. As with the green revolution in the last century, plant genetics …

Although long-term genetic gain has been achieved through increasing use of modern
breeding methods and technologies, the rate of genetic gain needs to be accelerated to …

Genomic selection (GS) is transforming the field of plant breeding and implementing models
that improve prediction accuracy for complex traits is needed. Analytical methods for …

Deep learning (DL) has emerged as a powerful tool to make accurate predictions from
complex data such as image, text, or video. However, its ability to predict phenotypic values …