Search Results (467)

Globally, Spodoptera frugiperda is a major threat to many important crops, including maize, rice, and cotton, causing significant economic damage. To control this invasive pest, environmentally friendly pest control techniques, including pheromone detection and identification of potential molecular targets to disrupt S. frugiperda mating communication, are needed. Female moths biosynthesize pheromones and emit them from the pheromone gland, which significantly depends on the intrinsic factors of the moth. Male S. frugiperda have a sophisticated olfactory circuit on their antennae that recognizes pheromone blends via olfactory receptor neurons (ORNs). With its potential to significantly modify the insect genome, CRISPR/Cas9 offers a revolutionary strategy to control this insect pest. The impairing physiological behaviors and disrupting the S. frugiperda volatile-sensing mechanism are the main potential applications of CRISPR/Ca9 explored in this review. Furthermore, the release of mutant S. frugiperda for their long-term persistence must be integral to the adoption of this technology. Looking forward, CRISPR/Cas9-based gene drive systems have the potential to synergistically target pheromone signaling pathways in S. frugiperda by disrupting pheromone receptors and key biosynthesis genes, thereby effectively blocking intraspecific communication and reproductive success. In conclusion, CRISPR/Cas9 provides an environmentally friendly and revolutionary platform for precise, targeted pest management in S. frugiperda. Full article

This study addressed the core challenge of intelligent pest and disease monitoring and early warning in smart horticultural production by proposing a multimodal deep learning framework based on multi-parameter environmental sensor arrays. The framework integrates visual information with electrical signals to overcome the inherent limitations of conventional single-modality approaches in terms of real-time capability, stability, and early detection performance. A long-term field experiment was conducted over 18 months in the Hetao Irrigation District of Bayannur, Inner Mongolia, using three representative horticultural crops—grape (Vitis vinifera), tomato (Solanum lycopersicum), and sweet pepper (Capsicum annuum)—to construct a multimodal dataset comprising illumination intensity, temperature, humidity, gas concentration, and high-resolution imagery, with a total of more than $2.6\times {10}^6$ recorded samples. The proposed framework consists of a lightweight convolution–Transformer hybrid encoder for electrical signal representation, a cross-modal feature alignment module, and an early-warning decision module, enabling dynamic spatiotemporal modeling and complementary feature fusion under complex field conditions. Experimental results demonstrated that the proposed model significantly outperformed both unimodal and traditional fusion methods, achieving an accuracy of $0.921$, a precision of $0.935$, a recall of $0.912$, an F1-score of $0.923$, and an area under curve (AUC) of $0.957$, confirming its superior recognition stability and early-warning capability. Ablation experiments further revealed that the electrical feature encoder, cross-modal alignment module, and early-warning module each played a critical role in enhancing performance. This research provides a low-cost, scalable, and energy-efficient solution for precise pest and disease management in intelligent horticulture, supporting efficient monitoring and predictive decision-making in greenhouses, orchards, and facility-based production systems. It offers a novel technological pathway and theoretical foundation for artificial-intelligence-driven sustainable horticultural production. Full article

Sustainable improvement of crop performance requires integrative approaches that link genomic variation to phenotypic expression through intermediate molecular pathways. Here, we present Reciprocal Best Linear Unbiased Prediction (Reciprocal BLUP), a predictability-guided multi-omics framework that quantifies the cross-layer relationships among the genome, metabolome, and microbiome to enhance phenotype prediction. Using a panel of 198 soybean accessions grown under well-watered and drought conditions, we first evaluated four direction-specific prediction models (genome → microbiome, genome → metabolome, metabolome → microbiome, and microbiome → metabolome) to estimate the predictability of individual omics features. We evaluated whether subsets of features with high cross-omics predictability improved phenotype prediction. These cross-layer models identify features that play physiologically meaningful roles within multi-omics systems, enabling the prioritization of variables that capture coherent biological signals enriched with phenotype-relevant information. Consequently, metabolome features were highly predictable from microbiome data, whereas microbiome predictability from metabolomic data was weaker and more environmentally dependent, revealing an asymmetric relationship between these layers. In the subsequent phenotype prediction analysis, the model incorporating predictability-based feature selection substantially outperformed models using randomly selected features and achieved prediction accuracies comparable to those of the full-feature model. Under drought conditions, the phenotype prediction models based on metabolomic or microbiomic kernels (MetBLUP or MicroBLUP) outperformed the genomic baseline (GBLUP) for several biomass-related traits, indicating that the environment-responsive omics layers captured phenotypic variations that were not explained by additive genetic effects. Our results highlight the hierarchical interactions among genomic, metabolic, and microbial systems, with the metabolome functioning as an integrative mediator linking the genotype, environment, and microbiome composition. The Reciprocal BLUP framework provides a biologically interpretable and practical approach for integrating multi-omics data, improving phenotype prediction, and guiding omics-based feature selection in plant breeding. Full article

The plant plasma membrane serves as the primary interface for perceiving extracellular signals, a function largely mediated by plasma membrane receptors (PMRs). In wheat (Triticum aestivum), the functional characterization of these receptors is impeded by the species’ large, hexaploid genome, which results in extensive gene duplication and functional redundancy. This review synthesizes current knowledge on wheat PMRs, covering their diversity, classification, and signaling mechanisms, with a particular emphasis on their central role in plant immunity. We highlight the remarkable structural and functional diversification of PMR families, which range in size from 10 members, as seen in the case of wheat leaf rust kinase (WLRK), to over 3424 members in the receptor-like kinase (RLK) family. Furthermore, we reviewed the role of PMRs in being critical for detecting a wide array of biotic stimuli, including pathogen-associated molecular patterns (PAMPs), herbivore-associated molecular patterns (HAMPs), and symbiotic signals. Upon perception, PMRs initiate downstream signaling cascades that orchestrate defense responses, including transcriptional reprogramming, cell wall reinforcement, and metabolic changes. The review also examines the complex cross-talk between immune receptors and other signaling pathways, such as those mediated by brassinosteroid and jasmonic acid receptors, which underpin the delicate balance between growth and defense. Finally, we bridge these fundamental insights to applications in crop improvement, delineating strategies like marker-assisted selection, gene stacking, and receptor engineering to enhance disease resistance. After identifying key obstacles such as genetic redundancy and pleiotropic effects, we propose future research directions that leverage multi-omics, systems biology, and synthetic biology to fully unlock the potential of wheat PMRs for sustainable agriculture. Full article

The rapid global expansion of genetically modified (GM) crops requires fast, on-site detection methods. Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated (CRISPR/Cas) systems offer a promising platform for decentralized GM organism (GMO) monitoring. This review focuses specifically on the application of this technology in agriculture and food supply chains, diverging from previous reviews centered on clinical diagnostics. We examine the mechanisms of key CRISPR effectors (e.g., Cas12a, Cas13a) and their integration into diagnostic platforms (e.g., DETECTR, SHERLOCK) for detecting transgenic elements (e.g., CaMV35S promoter). A dedicated comparison of signal readout modalities, including fluorescence, lateral flow, and electrochemical sensing, highlights their suitability for different GMO detection scenarios, from field screening to laboratory confirmation. Finally, we discuss current challenges, including multiplexing and standardization, and outline future directions, such as the engineering of novel Cas variants and integration with smartphone technology. CRISPR-based diagnostics are poised to become indispensable tools for decentralized, efficient, and reliable GMO detection. Full article

Ratoon rice cultivation is an efficient production system that achieves a second harvest from the stubble of the main crop, but its yield potential is largely constrained by variation in axillary bud regeneration capacity. Here, we identify OsPM19L, a plasma membrane–localized AWPM-19 domain protein, as a key regulator of rice ratooning ability. Transcriptome analysis revealed higher OsPM19L expression in strong-regeneration cultivars, followed by a sharp decline after harvest. Promoter assays and hormonal treatments demonstrated that OsPM19L is strongly induced by ABA and functions as a positive regulator in ABA signaling. Under field conditions, ospm19l mutants exhibited increased tiller number but reduced ratooning index, whereas OsPM19L-OE plants showed the opposite pattern, indicating stage-specific regulation of tillering and regeneration. Hormone profiling and gene expression analyses showed that OsPM19L is associated with altered levels of multiple phytohormones in regenerating axillary buds, showing higher CK and GA levels and lower IAA and ABA levels in OsPM19L-OE compared with the wild type. Consequently, OsPM19L appears to facilitate dormancy release and enhance early axillary bud growth during the ratoon season. These findings indicate OsPM19L may act as a central regulator linking ABA signaling with hormonal cross-talk, providing new insights into the molecular control of regeneration and potential targets for improving ratoon rice productivity. Full article

This study aims to elucidate the physiological and biochemical alterations induced by parasitic Cuscuta sp. (dodder) in lucerne (Medicago sativa L.), a key forage crop. Comparative analyses between infected and healthy plants revealed that significant reductions in chlorophyll a, b, and total chlorophyll, and protein levels in the leaf and stem tissues of Cuscuta-infested plants were evident. The parasitic infection led to increased activities in antioxidant enzymes such as catalase (CAT) and peroxidase (POX) in stems, but not in leaves. Phenolic compounds were significantly lower both in leaves and stems of dodder-infected lucerne plants. No statistically significant changes were detected in jasmonic acid (JA) and salicylic acid (SA) levels in both plant parts, suggesting that classical defense signaling pathways may not be predominantly activated under Cuscuta-mediated stress. Possibly, host defense might be impaired. Histological examinations demonstrated active structural defense responses, including localized tissue remodeling and the formation of callose-like structures at haustorial penetration sites. DNA fragmentations showed that Cuscuta-infected M. sativa plants exhibited slightly higher instability. Collectively, these findings provide novel insights into the molecular and biochemical basis of the Cuscuta-lucerne interactions and highlight the need for further investigation into host defense mechanisms. We assume that active defense structural parts at early growth stages of lucerne or hypersensitive-type responses occurring in the early penetration phase might fend off the invading holoparasite. The results also offer a valuable foundation for the development of Cuscuta-resistant lucerne cultivars and support the design of integrated, sustainable weed management strategies to mitigate the detrimental effects of parasitic plants on forage production systems. Full article

Solanum lycopersicum plants were grown in pots amended with biochar and PGPMs (plant growth-promoting microorganisms: Pseudomonas fluorescens and Azotobacter chroococcum), applied singularly and in combination, for three months, after which plants and soils were collected, divided into treatment groups based on organs, and analyzed. The following biochemical markers were studied: cellular respiration, shoot fresh and dry weight, root fresh weight, photosynthetic pigments (chlorophyll a, chlorophyll b, and carotenoids), membrane lipid peroxidation, proline content, total antioxidant capacity (DPPH and ABTS assay), hydrogen peroxide, ascorbic acid, total phenolic content, enzymatic activity (SOD, POD, CAT, and APX), total soluble sugar content, and total protein content. Also, soil parameters, such as pH, EC, total enzymatic activity, active carbon, and respiration, were measured. While biochar alone induced root H₂O₂ accumulation, its co-application with PGPMs turned this signal into a systemic trigger for defense, enhancing the antioxidant capacity and the production of proline, phenolics, and ascorbic acid without causing oxidative damage. At the soil level, microorganisms counteracted biochar’s inhibitory effects on enzymatic activity and intensified labile carbon use, indicating a more dynamic rhizosphere. Multivariate analysis confirmed that the combined treatment remodulated the plant–soil system, converting a stress factor into a resilience enhancer. This synergy underscores the role of biochar as an effective microbial carrier and PGPM consortia as bioactivators, together providing a powerful tool to prime crops against climate stress while preserving soil health. Full article

This paper proposes a free-space optical (FSO) system for early warning and detection of natural disasters. The system consists of multiple sensor nodes equipped with mirrors, motors, and controllers. Under normal weather conditions, signals are transmitted between nodes. However, in the presence of frost, signal reflection is directed to a base station (BS) by adjusting the mirrors’ orientation based on the reflection angle determined using Snell’s law. By monitoring the round-trip time of the signal to the BS, the frost-affected node can be identified. The power received at various nodes is analyzed, considering reflections from mirrors and path attenuation. The results indicate that sufficient power levels can be achieved for six nodes, covering an area of approximately 314.16 km². The total time required to send an alarm signal to the BS is calculated and compared with the systems proposed in the literature. The proposed system demonstrates a time reduction of up to 69.7% compared to systems where the signal traverses all nodes before reaching the BS and a 7% reduction compared to systems employing dedicated transmitters and receivers. The proposed system is easy to deploy in a crop area with a fast response and relatively low power consumption, making it an efficient solution for early warning of frost. To the author’s best knowledge, the proposed system is the first one to exploit mirrors in free-space optics for early warning of frost. The analysis presented in this paper is very helpful for further investigations into using mirrors in FSO systems for early warning and notification of natural disasters such as frost. Full article

During the climate change era, plants are increasingly exposed to multiple environmental challenges occurring simultaneously or sequentially. Among these, salt stress and waterlogging are two major factors that severely constrain crop productivity worldwide and often occur together. To survive under such conditions, plants have evolved sophisticated systems to scavenge harmful levels of reactive oxygen species (ROS). Despite their cytotoxic potential, ROS also act as key signaling molecules that interact with nitric oxide (NO), Ca²⁺, protein kinases, ion homeostasis pathways, and plant hormones. These signaling and acclimatory mechanisms are closely associated with the functions of energy-regulating organelles—chloroplasts and mitochondria—which are major sources of ROS under both individual and combined stresses. While many of these responses are shared between salt stress, waterlogging and their combination, it is likely that specific signaling mechanisms are uniquely activated when both stresses occur together—mechanisms that cannot be inferred from responses to each stress alone. Such specificity may depend on precise coordination among organelle-derived signals and the tight regulation of their cross-communication. Within this network, ROS and NO likely serve as central hubs, fine-tuning the integration of multiple signaling pathways that enable plants to adapt to complex and fluctuating stress environments. Full article

The production and productivity of cereal crops, which form the foundation of global food security, are increasingly threatened by unstable water regimes and recurring droughts linked to climate change. Fortunately, a wide diversity of cereal crops is endowed with natural resilience to drought and heat stress, enabling them to survive under conditions that are critical for other plants. Understanding the key morphological, genetic, physiological, biochemical, and ecological mechanisms—and their interactions—is crucial for unraveling the processes involved in drought tolerance in these species. A comprehensive study of cereal crops, their variability, and their ability to survive and thrive under arid conditions will unlock new opportunities for breeding drought-resistant agricultural varieties. This review highlights the role of root system architecture (RSA) and gravitropic mechanisms (e.g., EGT1, DRO1), the integration of phytohormonal crosstalk, the potential of wild relatives and genome editing, and the emerging role of plant growth-promoting rhizobacteria (PGPR) in enhancing drought resilience. We propose a novel synthesizing concept focused on overcoming the fundamental yield-survival trade-off by framing drought resilience through the lens of optimizing three interconnected functional modules: water budget architecture, metabolic homeostasis, and integrative signaling networks. The central advance of this framework is its systems-level perspective that redefines these well-studied components as dynamically interacting, tunable modules, providing a practical blueprint for designing crop ideotypes that break the yield-survival trade-off. Full article

Low Nitrogen Use Efficiency (NUE) remains a critical agricultural challenge, as an estimated 50–70% of applied nitrogen (N) is lost, resulting in negative environmental impacts and reduced crop production. To elucidate molecular mechanism controlling NUE in tomato (Solanum lycopersicum), we conducted a comprehensive genomic, transcriptomic, and functional analysis of the NPF, NRT2, and AMT transporter families under high-N commercial supply conditions. Our integrated analysis identified a shoot-to-root signaling mechanism where the plant’s metabolic performance systematically regulates N transport capacity. Under N sufficiency, the shoot exhibited reduced N assimilation, evidenced by NO₃⁻ accumulation (increased by 55.7%) and reduced Nitrate Reductase (NR) and Glutamine Synthetase (GS) activities (54.0% and 43.2% reduction, respectively), which correlated with a 42.3% reduction in chlorophyll synthesis capacity. This reduction in metabolic demand systematically triggered the downregulation of the key long-distance SlNPF transporters, SlNPF2.13 and SlNPF7.3, restricting N translocation and promoting significant root N accumulation (increased by 41.8%). Our data established that the leaf metabolic state is the systemic regulator of N transport and identified SlNPF2.13 and SlNPF7.3 as pivotal molecular checkpoints. These findings indicate that the manipulation of these transporters could serve as a valuable tool in molecular breeding programs to significantly enhance NUE in commercial tomato varieties. Full article

Global warming increases the frequency with which drought and heat stress occur simultaneously, especially in semi-arid regions. Such combined stress imposes a non-additive and more severe impact on plant growth, yield, and quality than either stress alone. Here, we integrate recent physiological, biochemical, and multi-omics studies to compare individual and combined stress responses and to dissect the underlying signal transduction networks. We show that drought-dominated phases rapidly elevate ABA concentrations and activate SnRK2–AREB cascades, whereas heat pulses trigger jasmonic acid and ethylene signals that antagonize ABA-driven stomatal closure. Under combined stress, these hormonal modules converge on a “competitive TF marketplace”, where ABA, JA, and GA cis-elements co-regulate invertase–sugar checkpoints, heat shock factor/ROS oscillators, and chromatin-remodeling events that determine reproductive fate. Recent advances using multi-omics approaches and systems biology have further elucidated these complex networks. These insights will inform future breeding strategies aiming to develop stress-tolerant crops. We highlight emerging tools—weighted gene co-expression networks, kinetic multi-omics, and cis-regulatory CRISPR editing—that can exploit these signaling hubs for breeding crops with improved combined stress tolerance. Full article

Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues of low efficiency of traditional monitoring methods and susceptibility of spectral signals to interference in field environments. Through comparative studies between experimental fields (n = 68) and large fields (n = 155), the performance of six vegetation indices was systematically evaluated, and optimized versions were designed. The study mainly found that the Yellow Rust Severity Index optimized (YRSI_O) index exhibited the best monitoring performance, with a field determination coefficient R² of 0.5713 (experimental field R² = 0.6118). The unmanned aerial vehicle (UAV) hyperspectral system combined with optimized vegetation index can effectively control spectral reflectance fluctuations, with a recognition accuracy of up to 85.2% in severely infected areas. This study also elucidated the three-stage physiological response mechanism of optimizing indicators on disease progression. This study provides key technical support for the practical application of hyperspectral technology in field monitoring of wheat stripe rust, and the proposed research method can be extended to other fields of crop disease monitoring. Full article

Agriculture is still at serious risk from viral infections, particularly in light of climate change and more intensive farming practices. Small non-coding RNAs (sRNAs), in particular microRNAs (miRNAs) and circular RNAs (circRNAs), have emerged as crucial post-transcriptional regulators of plant antiviral defense in this setting. These molecules provide an essential RNA-based immunity layer by regulating hormones, autophagy, redox balance, immunological signaling, and programmed cell death. In this work, we examine the molecular processes through which circRNAs and miRNAs function during viral infection, focusing on how they affect autophagy and systemic acquired resistance (SAR). Through thorough searches of PubMed, Web of Science, and Scopus, we combined findings from peer-reviewed experimental and transcriptomic studies. Our study covers important crops as well as model species (Arabidopsis thaliana, Nicotiana benthamiana), providing a thorough understanding of sRNA synthesis, target control, and antiviral signaling. By combining previously disparate data, this review provides a coherent framework for understanding how short RNAs affect plant immune responses to viral infections. We highlight key regulatory relationships that need further study and propose that these results can be used as a foundation for new RNA-based biotechnological approaches. By carefully altering RNA regulatory mechanisms, scientists can use this information to help them create more resistant crops. Full article