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Monohybrid Crosses

Monohybrid Crosses

Trihybrid Crosses

Trihybrid Crosses

Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal chance to...

Dihybrid Crosses

Dihybrid Crosses

Plant Breeding and Biotechnology

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Related Experiment Video

Updated: Jul 15, 2026

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

High Throughput Image-Based Phenotyping for Determining Morphological and Physiological Responses to Single and Combined Stresses in Potato

Published on: June 7, 2024

Phenomics-assisted sparse testing for potato breeding.

Alexandre Hild Aono¹, Aakash Chawade²

¹Department of Plant Breeding, Swedish University of Agricultural Sciences (SLU), SE-230 53, Alnarp, Sweden.

Scientific Reports

|July 13, 2026

Summary

Integrating image-based data into sparse testing improves potato breeding accuracy. This cost-effective strategy enhances predictive performance for tuber yield, even with limited resources.

Keywords:

Solanum tuberosum Genomic selection Genotype-by-environment interaction High-throughput phenotyping Multi-environment regression

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Last Updated: Jul 15, 2026

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Published on: June 7, 2024

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Published on: May 11, 2020

Area of Science:

Agricultural Science
Plant Breeding
Genetics

Background:

Global weather pattern shifts increase agricultural unpredictability.
Developing accurate plant selection strategies under uncertainty is crucial.
Sparse testing manages field trial costs but struggles with image data integration.

Purpose of the Study:

To develop a strategy for integrating high-throughput phenotyping data into sparse testing for potato breeding.
To enhance predictive performance in multi-environment trials using image-based data.
To improve the efficiency and accuracy of plant breeding selection strategies.

Main Methods:

Constructed an environmental kernel from the covariance matrix of image-based data.
Integrated high-throughput phenotyping data into sparse testing frameworks.
Assessed predictive performance of regression models using genomic, phenomic, and combined data.

Main Results:

Models using the environmental kernel achieved predictive accuracies comparable to or exceeding genomic prediction models.
The proposed approach showed strong performance for tuber yield prediction.
Image-based environmental kernels improved sparse testing efficiency and accuracy across scenarios.

Conclusions:

Image-based environmental kernels offer a cost-effective and scalable solution for improving sparse testing in plant breeding.
This strategy is particularly valuable for breeding programs with limited resources.
The findings support the use of high-throughput phenotyping data to enhance predictive breeding.