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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: closed (30 November 2025) | Viewed by 7215

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Special Issue Editor

Prof. Dr. Glauco Vieira Miranda

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Department of Agronomy, Federal University of Technology—Paraná, Santa Helena, Brazil
Interests: plant breeding; soil; plant; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of germplasm is essential for creating cultivars with new traits that differentiate them for competitive advantage in the market or for specific environments, enabling food production in all regions where human life exists. Furthermore, germplasm development needs to align with breeding programs, addressing future gaps in genetic variability required for new regions, emerging biotic and abiotic stresses, or even new biotechnologies that do not allow direct use of elite lineages.

In turn, the genetic improvement of corn with well-defined heterotic groups requires germplasm aligned with the heterosis implemented in the program, ensuring that the introduction of new genetic material maintains the genetic gains achieved in previous cycles. Additionally, identifying new traits within germplasm enables breeding programs to access new genetic variability, which can be incorporated to enhance cultivars for new regions or to increase productivity.

The development of germplasm and corn improvement can yield excellent results by applying and integrating a set of new technologies from both biotechnology and Industry 4.0 (artificial intelligence, visual computing, big data, etc.). These advancements enhance genetic gains through shorter selection cycles and smaller field populations or individuals, thereby reducing long-term costs and operations. High-throughput genotyping and phenotyping have enabled the evaluation of thousands of plants before they reach maturity or even before they are sown.

In this Special Issue, we aim to demonstrate the application of different biotechnologies, omics technologies, phenotyping, and corn breeding techniques that develop corn collection, select new germplasm, and enhance genetic gains and precision in germplasm development, catering to diverse environments and traits where corn cultivation takes place.

Prof. Dr. Glauco Vieira Miranda
Guest Editor

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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. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

genetics
phenotyping
artificial intelligence
omics technologies
breeding
genomics
transcriptomic
biotechnology
Corn cultivation techniques
Maize farming practices
Hybrid corn varieties
Genetically modified corn
Corn yield optimization
Sustainable corn production
Pest management
Corn disease resistance
Soil management
Irrigation practices
Climate impact on corn growth
Fertilization strategies
Crop rotation with corn
Corn harvesting methods
Post-harvest corn processing

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Published Papers (6 papers)

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Research

27 pages, 2816 KB

Open AccessArticle

CEHD: A Unified Framework for Detection and Height Estimation of Fresh Corn Ears in Field Conditions

by Hengyi Wang, Yang Li, Jun Fu, Qiankun Fu and Yongliang Qiao

Plants 2026, 15(1), 38; https://doi.org/10.3390/plants15010038 - 22 Dec 2025

Abstract

Real-time detection of fresh corn ear height can provide a basis for dynamic adjustment of harvester header parameters, reducing mechanical damage and improving harvest quality. This study proposes a corn ear height detection model (CEHD). A YOLO-HAMDF network is developed for ear recognition, in which the core modules—TBDA, GLSA, and AQE—respectively suppress background interference, enhance contextual perception, and optimize bounding-box scoring. Depth information is incorporated to filter non-target regions and improve system robustness. In addition, a DI-DeepSORT module is designed for ear tracking, where DBC-Net and IDA-Kalman, respectively, enhance the discriminability of ReID features and enable independent-dimension adaptive noise modeling with smoothed positional updates. Experimental results demonstrate that the proposed CEHD model achieves a mean absolute error (MAE) of only 3.21 ± 0.05 cm under field conditions, indicating strong stability and practical applicability. In summary, this study presents a stable and reliable corn ear height detection system, achieves real-time monitoring of ear height, and provides data support for the dynamic adjustment of header parameters in fresh corn harvesters. Full article

(This article belongs to the Special Issue Maize Cultivation and Improvement)

19 pages, 1687 KB

Open AccessArticle

Comparative Leaf Proteome Analysis of Maize (Zea mays L.) Exposed to Combined Drought and Heat Stress

by Cleopatra Pfunde, Charles Shelton Mutengwa, Graeme Bradley and Nyasha Esnath Chiuta

Plants 2025, 14(22), 3419; https://doi.org/10.3390/plants14223419 - 8 Nov 2025

Viewed by 553

Abstract

This study sought to screen 45 maize (Zea mays L.) inbred lines for tolerance to combined drought and heat stress (CDHS) and identify the leaf proteome patterns of two inbred lines with contrasting stress response at early vegetative stage. Biomass accumulation was significantly reduced under CDHS compared to optimum conditions. Furthermore, CDHS-tolerant inbred lines exhibited significantly lower (p < 0.05) leaf temperatures (28.6 °C) and higher sub-stomatal CO₂ concentration (9012 mol mol⁻¹) and photosynthetic yield (0.69) under stress. The tolerant (CIM18) and susceptible (QS21) inbred lines were exposed to stress by maintaining a field capacity of 25% for 7 days and increasing the daily ambient temperature by 5 °C from 25 °C to 40 °C. Conventional two-dimensional electrophoresis analysis was used to compare leaf protein expression profiles, and significant differences (p < 0.05) were observed. Out of a total of 505 proteins, 114 showed significant quantitative variation. Of these, 62 proteins had a twofold upregulation in CIM18, while 52 were downregulated. Twenty upregulated proteins were selected for amino acid micro-sequencing, and 11 proteins were uniquely expressed in CIM18. The other nine proteins had ≥ twofold upregulation in CIM18 compared to QS21. The functions of the identified proteins included defence, metabolism, photosynthesis and structure. Full article

(This article belongs to the Special Issue Maize Cultivation and Improvement)

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18 pages, 1953 KB

Open AccessArticle

Genetic Gains and Field Validation of Synthetic Populations in Tropical Maize Using Selection Indexes and REML/BLUP

by Antônia Maria de Cássia Batista de Sousa, Marcela Pedroso Mendes Resende, Ailton Jose Crispim-Filho, Glauco Vieira Miranda and Edésio Fialho dos Reis

Plants 2025, 14(20), 3149; https://doi.org/10.3390/plants14203149 - 13 Oct 2025

Viewed by 648

Abstract

The development of tropical maize populations with high heterosis potential is essential for sustaining genetic progress in hybrid breeding programs, yet accurate selection remains challenging due to genotype–phenotype interactions and inbreeding depression. This study evaluated the efficiency of five selection strategies in recurrent selection programs using F₂ populations derived from commercial maize hybrids: Smith–Hazel Index (SHI), Base Index (BIA), Mulamba–Mock Index (MMI), REML/BLUP for grain yield (BLUP_GY), and REML/BLUP for inbreeding depression (BLUP_ID). Consistency among methods was assessed with a heatmap, and predicted genetic gains were compared with realized field performance. Predicted gains were highest with MMI and BIA for grain yield and ear weight, although realized results revealed discrepancies, particularly for BLUP-based approaches. Notably, BLUP_GY, which had the lowest predicted yield (4025 kg ha⁻¹), achieved a realized yield of 5620 kg ha⁻¹, surpassing BIA and SHI. This indicates that additive potential was underestimated in predictions, likely due to dominance and environmental effects in early F₂ cycles. Overall, BLUP-based methods proved effective in identifying progenies with higher additive value, and their integration with phenotypic indices is recommended to combine short-term realized gains with sustained genetic improvement. Full article

(This article belongs to the Special Issue Maize Cultivation and Improvement)

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13 pages, 1752 KB

Open AccessArticle

The Identification of a Single-Base Mutation in the Maize Dwarf 1 Gene Responsible for Reduced Plant Height in the Mutant 16N125

by Ping Wang, Bingbing Liang, Zhengjun Li, Huaiyu Dong, Lixia Zhang and Xiaochun Lu

Plants 2025, 14(8), 1217; https://doi.org/10.3390/plants14081217 - 15 Apr 2025

Viewed by 1204

Abstract

Maize (Zea mays L.) is a globally vital crop for food, feed, and biofuel production, with plant height (PH) being a key agronomic trait that significantly influences yield, lodging resistance, and stress tolerance. This study identified a single-base mutation in the D1 (Dwarf 1) gene responsible for the dwarf phenotype in the maize mutant 16N125. Through genetic analysis and fine mapping, the candidate region was localized to chromosome 3, narrowing it down to an interval containing three genes. Sequencing revealed a non-synonymous mutation in D1, which encodes a gibberellin 3-beta-dioxygenase, leading to amino acid substitutions at positions 61 and 123. Genetic analysis of F2 populations confirmed that the mutation at position 61 was responsible for the dwarf trait. Furthermore, the mutation was detected in several Chinese inbred lines, indicating its potential role in dwarfing under specific conditions. These findings provide critical insights into the genetic mechanisms regulating maize plant height, offering valuable information for breeding programs focused on improving crop architecture and yield to address the challenges of global food security and climate change. Full article

(This article belongs to the Special Issue Maize Cultivation and Improvement)

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13 pages, 2243 KB

Open AccessArticle

Enhancing Across-Population Genomic Prediction for Maize Hybrids

by Guangning Yu, Furong Li, Xin Wang, Yuxiang Zhang, Kai Zhou, Wenyan Yang, Xiusheng Guan, Xuecai Zhang, Chenwu Xu and Yang Xu

Plants 2024, 13(21), 3105; https://doi.org/10.3390/plants13213105 - 4 Nov 2024

Viewed by 2090

Abstract

In crop breeding, genomic selection (GS) serves as a powerful tool for predicting unknown phenotypes by using genome-wide markers, aimed at enhancing genetic gain for quantitative traits. However, in practical applications of GS, predictions are not always made within populations or for individuals that are genetically similar to the training population. Therefore, exploring possibilities and effective strategies for across-population prediction becomes an attractive avenue for applying GS technology in breeding practices. In this study, we used an existing maize population of 5820 hybrids as the training population to predict another population of 523 maize hybrids using the GBLUP and BayesB models. We evaluated the impact of optimizing the training population based on the genetic relationship between the training and breeding populations on the accuracy of across-population predictions. The results showed that the prediction accuracy improved to some extent with varying training population sizes. However, the optimal size of the training population differed for various traits. Additionally, we proposed a population structure-based across-population genomic prediction (PSAPGP) strategy, which integrates population structure as a fixed effect in the GS models. Principal component analysis, clustering, and Q-matrix analysis were used to assess the population structure. Notably, when the Q-matrix was used, the across-population prediction exhibited the best performance, with improvements ranging from 8 to 11% for ear weight, ear grain weight and plant height. This is a promising strategy for reducing phenotyping costs and enhancing maize hybrid breeding efficiency. Full article

(This article belongs to the Special Issue Maize Cultivation and Improvement)

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13 pages, 439 KB

Open AccessArticle

Semi-Arid Environmental Conditions and Agronomic Traits Impact on the Grain Quality of Diverse Maize Genotypes

by Nicolás Francisco Bongianino, María Eugenia Steffolani, Claudio David Morales, Carlos Alberto Biasutti and Alberto Edel León

Plants 2024, 13(17), 2482; https://doi.org/10.3390/plants13172482 - 5 Sep 2024

Cited by 2 | Viewed by 1486

Abstract

We assessed the impact of environmental conditions and agronomic traits on maize grain quality parameters. The study was conducted using genotypes with distinct genetic constitutions developed specifically for late sowing in semi-arid environments. We evaluated the agronomic, physical, and chemical characteristics of eight maize open-pollinated varieties, six inbred lines, and three commercial hybrids. The yield of the open-pollinated varieties showed a positive correlation with protein content (r = 0.33), while it exhibited a negative correlation with the carbohydrate percentage (r = −0.36 and −0.42) in conjunction with the inbred lines. The flotation index of the hybrids was influenced primarily by the environmental effect (50.15%), whereas in the inbred lines it was nearly evenly divided between the genotype effect (45.51%) and the environmental effect (43.15%). In the open-pollinated varieties, the genotype effect accounted for 35.09% and the environmental effect for 42.35%. The characteristics of plant structure were associated with grain quality attributes relevant for milling, including hardness and test weight. Inbred lines exhibited significant genotype contributions to grain hardness, protein, and carbohydrate content, distinguishing them from the other two germplasm types. These associations are crucial for specific genotypes and for advancing research and development of cultivars for the food industry. Full article

(This article belongs to the Special Issue Maize Cultivation and Improvement)

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