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Fusarium Head Blight (FHB), a fungal wheat (Triticum aestivum) disease that threatens global food security, requires precise quantification of diseased spikelet rate (DSR) as a phenotypic indicator for resistance breeding. Most techniques for measuring DSR rely on manual spikelet-by-spikelet observation and counting, which is inefficient and destructive. Although deep learning offers great promise for automated DSR measurement, existing intelligent detection algorithms are hampered by the lack of spikelet-level annotated data, insufficient feature representation for diseased spikelets, and weak spatial encoding of densely arranged spikelets. To address these challenges, we constructed a dataset of 620 high-resolution RGB images of wheat spikes with 5,222 spikelet-level annotations to systematically analyze spikelet size distributions to fill small-object detection data gaps in this field. We designed FHBDSR-Net, a light framework for automated DSR measurement centered on diseased spikelet detection, which features (1) multi-scale feature enhancement architecture that dynamically combines lesion textures, morphological features, and lesion-awn contrast through adaptive multi-scale kernels to suppress background noise; (2) the Inner-EfficiCIoU loss function to reduce small-target localization errors in dense contexts; and (3) a scale-aware attention module using dilated convolutions and self-attention to encode multi-scale pathological patterns and spatial distributions to enhance dense spikelet resolution. FHBDSR-Net detected diseased spikelets with an average precision of 93.8% with a lightweight design of 7.2 M parameters. The results were strongly correlated with expert evaluations, with a Pearson correlation coefficient of 0.901. Our method is suitable for deployment on resource-constrained mobile devices, facilitating portable plant phenotyping and smart breeding.

Pearl millet (Pennisetum glaucum) is a major staple food in arid and semi-arid regions of sub-Saharan Africa, India, and South Asia. However, how epigenetic mechanisms regulate tissue-specific gene expression in this crop remains poorly understood. In this study, we profiled multiple epigenetic features in the young panicles and roots of pearl millet using RNA-seq, ATAC-seq, whole-genome bisulfite sequencing, and ChIP-seq (H3K4me3 and H3K36me3). We identified thousands of genes that were differentially expressed between these two tissues. Root-specific genes were enriched for plant hormone signaling, oxidative phosphorylation, and stress responses. Analysis of chromatin accessibility revealed that root-specific accessible chromatin regions (ACRs) were enriched in binding motifs for stress-responsive transcription factors (e.g., NAC, WRKY), whereas ACRs in young panicles were enriched in motifs for developmental regulators (e.g., AP2/ERF). DNA methylation profiling revealed 25,141 tissue-specific differentially methylated regions, with CHH methylation—rather than CG or CHG methylation—showing the strongest tissue specificity. Promoters of root-specific genes had higher levels of CHH methylation compared to those of young panicle–specific genes, suggesting that the roles of CHH methylation in regulating transcription might be tissue dependent. Notably, promoter-associated H3K4me3 marked panicle-specific genes, whereas root-specific expression was primarily linked to chromatin accessibility, suggesting a transcription factor–mediated regulatory mechanism. Together, our findings highlight the distinct epigenetic frameworks governing tissue-specific gene expression in pearl millet and provide valuable insights for advancing the genetic improvement of this crop.

Alfalfa (Medicago sativa), a globally important crop known for its high yields, wide adaptability, and high protein content, provides an excellent feed source for livestock. Smart breeding, an emerging technology that integrates genomics and phenomics, holds considerable promise for accelerating the development of elite varieties of alfalfa. Nevertheless, there are few phenotypic analysis tools available for alfalfa. Here, we present DU-Net-L, an effective and lightweight model for segmenting alfalfa images that enables preliminary analysis of branch phenotypes based on digital images. In our study, the DeepLabV3+ model struggled to handle petioles, while U-Net performed poorly with images captured under high light. To address these issues, we have created a new model utilizing ResNet34 as its feature extraction module and retaining the architectures of both DeepLabV3+ and U-Net. An analysis based on test data indicated that the new model overcame the shortcomings of using either of the two base models individually. Subsequently, we lightened the fused model by reducing output channels in each block, while maintaining its predictive capability. We have named the lightened model DU-Net-L. Ultimately, we adopted an exponential decay strategy for the learning rate and increased the number of training epochs to select an optimal parameter combination. This approach achieved 99.83% accuracy and a mean intersection over union of 0.9411, with a size of 25.42 MB. In summary, we have provided a lightweight model that effectively segments stems and leaves in alfalfa images, fulfilling the requirements for the preliminary analysis of branch phenotypes.

Agronomic traits in maize (Zea mays L.) are complex and modulated by pleiotropic loci and interconnected genetic networks. However, the traditional single-trait genome-wide association study (GWAS) method often misses genetic associations among traits, overlooks pleiotropic effects, and underestimates shared regulatory mechanisms. In the current study, we employed multi-trait analysis of GWAS (MTAG) and constructed a genetic network to dissect the genetic architecture of 18 agronomic traits across a genetically diverse panel of 2,448 maize inbred lines. Incorporating MTAG significantly improved the detection of pleiotropic loci that had not been detected by single-trait GWAS. Using a genetic network, we uncovered numerous previously unrecognized connections among traits related to plant architecture, yield, and flowering time. The 49 detected hub nodes, including Zm00001d028840 and Zm00001d033859 (knotted1), influence multiple traits. Co-expression analysis of candidate genes across two developmental stages validated their distinct yet complementary roles, with Zm00001d028840 linked to early cell wall remodeling and Zm00001d033849 involved in chromatin remodeling during tasseling. Moreover, we integrated results from GWAS, MTAG, and genetic network analyses to prioritize pleiotropic loci and hub genes that regulate multiple agronomic traits. This integrative approach offers a practical framework for selecting stable, multi-trait-associated targets, thereby supporting more precise and efficient crop improvement strategies.

Synonymous mutations have traditionally been regarded as functionally neutral because they do not alter protein sequences. However, growing evidence suggests these variants can affect gene expression, RNA structure, and protein function, ultimately influencing phenotypes. A recent study by Xin et al. (2025) provides strong evidence that synonymous mutations can exert regulatory effects through epitranscriptomic mechanisms, particularly m⁶A RNA methylation. The authors identify a synonymous 1287C > T mutation in the ACS2 gene that reduces m⁶A methylation at the adjacent A¹²⁸⁶ site. This reduction alters RNA secondary structure, creating a more compact conformation that impairs translation efficiency, leading to decreased ACS2 protein levels and promoting fruit elongation in cultivated cucumbers. The mutation lies within a domestication sweep region and ACS2^1287C is exclusively found in wild cucumber populations, suggesting that ACS2^1287T has been favored during domestication for its agronomic benefits. Notably, the study also uncovers a genotype-dependent interaction between ACS2 and the m⁶A reader protein YTH1, which binds only to methylated transcripts, further illustrating how genetic background modulates epitranscriptomic regulation. These findings challenge the long-standing assumption that synonymous variants are biologically irrelevant and introduce RNA methylation as a key, dynamic regulatory layer in crop domestication and breeding, offering new opportunities for RNA-based precision breeding.

Cis-regulatory elements (CREs) are the genetic DNA fragments bound by transcription factors (TFs). CREs function as molecular switches that precisely modulate the dosage and spatiotemporal patterns of gene expression. The systematic identification of CREs not only facilitates the annotation of the functional non-coding genome but also provides essential insights into the architecture of gene regulatory networks and sheds light on an accurate selection of the target sites for genetic engineering of crops. In this review, we summarize the current high-throughput methodologies used for identifying CREs, illustrate the associations between CREs and agronomic traits in horticultural crops, and discuss how CREs can be exploited to facilitate crop breeding.

In the plant innate immune system, pattern recognition receptor (PRR) and nucleotide-binding domain leucine-rich repeat (NLR) proteins recognize pathogens and activate defenses. To prevent excessive immune responses that could affect growth, plants regulate PRRs and NLRs at the transcriptional and post-transcriptional levels. Poised or bivalent chromatin states, marked by the simultaneous presence of active and repressive epigenetic modifications, maintain genes in a transcriptionally primed state, keeping their expression low while enabling their rapid activation in response to stress. Here, we investigated how poised chromatin states regulate PRR and NLR genes in soybean (Glycine max). Our integrative epigenomic and transcriptomic analysis revealed that although NLR and PRR genes both harbor abundant active and repressive histone modifications and exhibit high chromatin accessibility, their basal expression levels remain relatively low. Moreover, clustered NLR and PRR genes residing within the same topologically associating domains shared similar chromatin states and expression dynamics, suggesting coordinated control. These gene families had distinct epigenetic features: NLR genes displayed narrow H3K27me3 peaks together with strong pausing of RNA Polymerase II at their 5′ ends, whereas PRR genes were characterized by broader H3K27me3 peaks. Together, our results shed light on the role of poised chromatin states in coordinating growth and defense responses in soybean.

The emerging field of epitranscriptomics has revolutionized our understanding of post-transcriptional regulation in plant systems. This review focuses on cutting-edge discoveries in the area of RNA modification, with a particular emphasis on the N⁶-methyladenosine (m⁶A)-mediated regulatory networks that govern plant development and fruit maturation. We systematically summarize the spatiotemporal patterns of RNA modifications and their integration into phytohormone signaling cascades and responses to environmental stimuli. Advanced epitranscriptome sequencing platforms have identified evolutionarily conserved modification signatures across angiosperm species, while simultaneously revealing species-specific regulatory architectures. Despite substantial progress, our understanding of the molecular mechanisms that underlie RNA modifications, especially those other than m⁶A, remains limited. We propose an innovative roadmap that combines CRISPR-based writer/eraser manipulation, single-cell spatial epitranscriptomics, and synthetic biology approaches to harness RNA modification networks for precision agriculture. We also underscore the importance of interdisciplinary collaboration that integrates findings from biology, chemistry, physics, and computer science to decode the plant epitranscriptome. To enable precise control of postharvest physiology, future priorities should include the development of biosensors for specific modification types, the engineering of RNA modification–dependent translation control systems, and the development of RNA epigenetic editing tools.

Soybean (Glycine max), an exceptionally nutritious crop rich in high-quality proteins and oils, is extensively used in various food products. Aromatic varieties of soybeans are in particular demand. Characterized by its distinctive popcorn-like aroma, 2-acetyl-1-pyrroline (2-AP) is an important volatile compound present in soybeans and other plants. The enzyme betaine aldehyde dehydrogenase (BADH) is closely associated with 2-AP production. However, the transcriptional regulatory network that governs BADH gene expression in soybean remains undefined. In this study, we determined that the transcript levels of the BADH gene, GmBADH2, vary significantly across different soybean organs and differ markedly from those of GmBADH1. We showed that GmMYB93 is a transcriptional repressor that directly regulates the expression of GmBADH2 by binding to the CAGTTA elements in its promoter. Furthermore, the silencing of GmMYB93 significantly reduced 2-AP accumulation in soybeans. Our findings shed light on the genetic mechanisms underlying soybean aroma formation and lay a foundation for developing novel aromatic soybean varieties.