The first paradigm of plant breeding involves direct selection-based phenotypic observation,
followed by predictive breeding using statistical models for quantitative traits constructed …

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

Crop yield forecasting depends on many interactive factors, including crop genotype,
weather, soil, and management practices. This study analyzes the performance of machine …

Classical univariate and multivariate statistics are the most common methods used for data
analysis in plant breeding and biotechnology studies. Evaluation of genetic diversity …

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety,
plant density, available water, nutrients, and planting date; these are the main factors that …

Climate change is posing new challenges to crop-related concerns, including food
insecurity, supply stability, and economic planning. Accurately predicting crop yields is …

Introduction In climate change, breeding crop plants with improved productivity,
sustainability, and adaptability has become a daunting challenge to ensure global food …

For a globally recognized plant breeding organization, manually recorded field observation
data is crucial for plant breeding decision making. However, certain phenotypic traits such …

We investigate the predictive performance of two novel CNN-DNN machine learning
ensemble models in predicting county-level corn yields across the US Corn Belt (12 states) …

The crop variety yield prediction is widely used to select new varieties and select suitable
planting areas for them, but it still suffers from multiple grand challenges, including sparse …