Repository for version control of the BrAPI specifications

Utilities for field trial analysis.

📈🌱 Mixed Models for Agriculture in R

Code for "Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials" (Fernandes et. al 2024)

We present here a 1D convolutional neural network model to predict grain protein content using spectroscopic data of multiple cereals

Using near-infrared spectroscopy (NIRS) and machine learning to determine oleic acid content from peanut raw grains

Modeling of nutritional traits from multiple crops using NIRS and machine learning/statistics

Diallel analysis of carrot phenotypes

📉🌱 R für Bio- und Agrarwissenschaftler

Tools and Statistical Procedures in Plant Science

Analysis of Selection Index in Plant Breeding

List of papers related to Agricultural Statistics, Plant Breeding, and Quantitative Genetics

A Python library for Statistical Analysis and Simulations of Plant Breeding Experiments, designed specifically for researchers and students who are new to programming but want to understand field design concepts through practical implementation.

A Practical Haplotype Graph (PHG) for apple (Malus domestica)

Data repository for the paper of the same name.

Gradient Boosting Regression (GBR, https://github.com/scikit-learn/scikit-learn) model for genomic predictions

Principal Component Analysis, PCA, Gaussian Markov Random Fields, Graphical model,

This repository contains a machine learning model designed to classify different landrace bean varieties based on their biometric and growth characteristics. Using Python, the model applies advanced classification algorithms to distinguish bean varieties accurately.

This repository serves to document, share, and collaborate on scripts used in the University of Arkansas fruit breeding lab.

A Breeding Api (BrAPI) implementation written in Java. Contains all data objects, client and server interfaces for all modules.