3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.

Engineering the Arabidopsis cell-surface immune receptor RECEPTOR-LIKE PROTEIN 23 (RLP23) enhances plant immunity by enabling functional signaling triggered by the conserved pathogen peptide nlp20. Structural optimization enables broad-spectrum disease resistance across species without compromising growth, demonstrating the potential of rationally reprogrammed pattern recognition receptors as a durable and sustainable strategy for crop protection.

The development of efficient and precise genome-editing tools is crucial for advancing functional genomics and improving crops. Our previously established nuclease-mediated prime editing (NM-PE) system, which combines the SpCas9 nuclease with prime editing based on microhomology-mediated end joining, enables the seamless insertion of small DNA fragments into plant genomes to add tags to genes of interest. However, the efficiency of 3 × FLAG sequence insertion via NM-PE requires further improvement. Here, we report a significant optimization of this system by replacing the M-MLV reverse transcriptase (RT) with evolved variants of the retrotransposon RT Tf1 derived from the mammalian PE6 system. Through codon optimization, we generated the evoTf1M4 variant, which substantially enhanced the efficiency of NM-PE. The optimized construct rPE20aV3 achieved up to 18.75% precise insertion of a 66-bp 3 × FLAG sequence at endogenous loci, representing a three-fold improvement over the original NM-PE system. Our results demonstrate that Tf1-aided optimization of NM-PE serves as an efficient platform for seamless insertion of a 3 × FLAG sequence in rice, offering broad potential for advanced genome engineering in plants.

Whole-genome duplication (WGD) events have occurred in numerous branches of the tree of life and are particularly prominent within plant lineages. The significant disparity in the frequency of WGD events across different species imposes specific technical requirements on collinearity alignment tools. Here we present quota_Anchor, a tool for conducting strand- and WGD-aware collinear gene identification. This tool was designed to strictly control the alignment depth and directionality of adjacent gene pairs when searching collinear blocks. We explored the ability of quota_Anchor to position WGD events and classify homologous genes based on different gene duplication modes. User-friendly modules were employed for generating high-quality plots, which revealed that relatively inverted gene pairs tend to exhibit less similar expression patterns than non-relatively inverted gene pairs. Notably, we determined that WGD-derived genes are more frequently associated with plant growth and developmental processes, while genes derived from tandem duplication and proximal duplication are often enriched in stress response and defense-related functions. The accuracy and flexibility of quota_Anchor make it a powerful toolkit for comparative genomics research.

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.Supplementary InformationThe online version contains supplementary material available at 10.1007/s42994-025-00235-2.

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.

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.

Meeting the increasing demand for food and industrial products by the growing global population requires targeted efforts to improve crops, livestock, and microorganisms. Modern biotechnology, particularly genetic modification (GM) and genome-editing (GE) technologies, is crucial for food security and environmental sustainability. China, which is at the forefront of global biotechnological innovation and the rapid advancements in GM and GE technologies, has prioritized this field by implementing strategic programs such as the National High-tech Research & Development Program in 1986, the National Genetically Modified Organism New Variety Breeding Program in 2008, and the Biological Breeding-National Science and Technology Major Project in 2022. Many biotechnological products have been widely commercialized in China, including biofertilizers, animal feed, animal vaccines, pesticides, and GM crops such as cotton (Gossypium hirsutum), maize (Zea mays), and soybean (Glycine max). In this review, we summarize progress on the research and utilization of GM and GE organisms in China over the past 3 decades and provide perspectives on their further development. This review thus aims to promote worldwide academic exchange and contribute to the further development and commercial success of agricultural biotechnology.

Plant specialized metabolites are commonly stored in glycosylated forms within plant cells, with their homeostasis regulated by glycosyltransferases and β-glucosidases (BGLUs, also known as β-glucoside hydrolases (E.C.3.2.1.21)). Soyasaponins, the predominant triterpenoid compounds (C30) in soybean seeds, contain two sugar moieties attached at the C3 and C22 positions. While glycosyltransferases involved in soyasaponin biosynthesis have been well characterized, the role of BGLUs in soyasaponin homeostasis remains unclear. In this study, we identified GmSSBG1 (Soyasaponin β-glucosidase1; Glyma.07G258700) as a candidate gene potentially involved in soyasaponin homeostasis through gene to gene co-expression analysis. Biochemical assays demonstrated that GmSSBG1 specifically hydrolyzes arabinose residues at the C22 position of A0- and B0-series soyasaponins. Loss-of-function mutations in GmSSBG1 led to a significant accumulation of A0- and B0-series soyasaponins in mutant seeds, which correlated with a pronounced decrease in resistance to the soybean pod borer (Leguminivora glycinivorella). Our findings provide critical insights into the regulatory mechanisms underlying soyasaponin homeostasis and lay a theoretical foundation for molecular breeding strategies aimed at developing soybean lines with enhanced resistance to soybean pod borer, even to other insect pests.