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Agronomy
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Innovations and Prospects for Future Agriculture: Applications of Machine Learning...
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Innovations and Prospects for Future Agriculture: Applications of Machine Learning and AI in Crop Breeding

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Crop Breeding and Genetics".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 967

Special Issue Editors

Dr. Moysés Nascimento

E-Mail Website
Guest Editor
Department of Statistics, Federal University of Viçosa, Viçosa 36570-900, MG, Brazil
Interests: statistical methods applied to breeding (plants and animals); computational intelligence; statistical learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Carolina Nascimento

E-Mail Website
Guest Editor
Departamento de Estatística, Universidade Federal de Viçosa, Viçosa 36570-260, MG, Brazil
Interests: genomic selection; association analysis; applied statistics; plant breeding; multivariate statistics; regression models; computational intelligence; adaptability and stability; mixed model

Special Issue Information

Dear Colleagues,

The integration of machine learning (ML) and artificial intelligence (AI) into plant breeding represents a significant leap forward, driven by the historical imperative to enhance agricultural productivity and resilience. Faced with the challenges of feeding a global population amidst the growing pressures of climate change and dwindling natural resources, the application of computational intelligence offers opportunities. This Special Issue aims to explore the scope of how ML and AI can improve plant breeding practices. We seek to showcase cutting-edge research that demonstrates novel algorithms, methodologies, and applications of AI and ML in areas such as precision phenotyping, genomic selection, disease and pest resistance breeding, stress tolerance enhancement, and the optimization of breeding programs. We are soliciting original research articles, reviews, and perspectives that highlight innovative approaches, significant findings, and future directions in this rapidly evolving field.

Prof. Dr. Moysés Nascimento
Dr. Ana Carolina Nascimento
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational intelligence
  • plant breeding
  • climate changes
  • precision phenotyping
  • genomic selection

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Published Papers (1 paper)

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Research

16 pages, 3176 KB  
Article
Stacking Ensemble Learning for Genomic Prediction Under Complex Genetic Architectures
by Maurício de Oliveira Celeri, Moyses Nascimento, Ana Carolina Campana Nascimento, Filipe Ribeiro Formiga Teixeira, Camila Ferreira Azevedo, Cosme Damião Cruz and Laís Mayara Azevedo Barroso
Agronomy 2026, 16(2), 241; https://doi.org/10.3390/agronomy16020241 - 20 Jan 2026
Viewed by 554
Abstract
Genomic selection (GS) estimates the GEBV from genome-wide markers to reduce generation intervals and optimize germplasm selection, which is particularly advantageous for high-cost or late-expressed traits. While models like GBLUP are popular, they assume a polygenic architecture. In contrast, the Bayesian alphabet and [...] Read more.
Genomic selection (GS) estimates the GEBV from genome-wide markers to reduce generation intervals and optimize germplasm selection, which is particularly advantageous for high-cost or late-expressed traits. While models like GBLUP are popular, they assume a polygenic architecture. In contrast, the Bayesian alphabet and machine learning (ML) can accommodate other types of genetic architectures. Given that no single model is universally optimal, stacking ensembles, which train a meta-model using predictions from diverse base learners, emerge as a compelling solution. However, the application of stacking in GS often overlooks non-additive effects. This study evaluated different stacking configurations for genomic prediction across 10 simulated traits, covering additive, dominance, and epistatic genetic architectures. A 5-fold cross-validation scheme was used to assess predictive ability and other evaluation metrics. The stacking approach demonstrated superior predictive ability in all scenarios. Gains were especially pronounced in complex architectures (100 QTLs, h2 = 0.3), reaching an 83% increment over the best individual model (BayesA with dominance), and also in oligogenic scenarios with epistasis (10 QTLs, h2 = 0.6), with a 27.59% gain. The success of stacking was attributed to two key strategies: base learner selection and the use of robust meta-learners (such as principal component or penalized regression) that effectively handled multicollinearity. Full article
(This article belongs to the Special Issue Innovations and Prospects for Future Agriculture: Applications of Machine Learning and AI in Crop Breeding)
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