Search Results (870)

The cereal cyst nematode, Heterodera avena, is responsible for substantial economic losses in the global production of wheat, barley, and other cereal crops. Extracellular enzymes, particularly those from the glycoside hydrolase 18 (GH18) family, such as chitinases secreted by Trichoderma spp., play a crucial role in nematode control. However, the genome-wide analysis of Trichoderma longibrachiatum T6 (T6) GH18 family genes in controlling of H. avenae remains unexplored. Through phylogenetic analysis and bioinformatics tools, we identified and conducted a detailed analysis of 18 GH18 genes distributed across 13 chromosomes. The analysis encompassed gene structure, evolutionary development, protein characteristics, and gene expression profiles following T6 parasitism on H. avenae, as determined by RT-qPCR. Our results indicate that 18 GH18 members in T6 were clustered into three major groups (A, B, and C), which comprise seven subgroups. Each subgroup exhibits highly conserved catalytic domains, motifs, and gene structures, while the cis-acting elements demonstrate extensive responsiveness to hormones, stress-related signals, and light. These members are significantly enriched in the chitin catabolic process, extracellular region, and chitinase activity (GO functional enrichment), and they are involved in amino sugar and nucleotide sugar metabolism (KEGG pathway enrichment). Additionally, 13 members formed an interaction network, enhancing chitin degradation efficiency through synergistic effects. Interestingly, 18 members of the GH18 family genes were expressed after T6 parasitism on H. avenae cysts. Notably, GH18-3 (Group B) and GH18-16 (Group A) were significantly upregulated, with average increases of 3.21-fold and 3.10-fold, respectively, from 12 to 96 h after parasitism while compared to the control group. Meanwhile, we found that the GH18-3 and GH18-16 proteins exhibit the highest homology with key enzymes responsible for antifungal activity in T. harzianum, demonstrating dual biocontrol potential in both antifungal activity and nematode control. Overall, these results indicate that the GH18 family has undergone functional diversification during evolution, with each member assuming specific biological roles in T6 effect on nematodes. This study provides a theoretical foundation for identifying novel nematicidal genes from T6 and cultivating highly efficient biocontrol strains through transgenic engineering, which holds significant practical implications for advancing the biocontrol of plant-parasitic nematodes (PPNs). Full article

The integration of Artificial Intelligence (AI) into remote sensing data classification has revolutionized agriculture and environmental monitoring. AI is one of the main technologies used in smart farming that enhances and optimizes the sustainability of agricultural production. The use of AI in agriculture can involve land use mapping and crop detection, crop yield monitoring, flood-prone area detection, pest disease monitoring, droughts prediction, soil content analysis and soil production capacity detection, and for monitoring the evolution of forests and vegetation. This review examines recent advancements in AI-driven classification techniques for various applications regarding agriculture and environmental monitoring to answer the following research questions: (1) What are the main problems that can be solved through incorporating AI-driven classification techniques into the field of smart agriculture and environmental monitoring? (2) What are the main methods and strategies used in this technology? (3) What type of data can be used in this regard? For this study, a systematic literature review approach was adopted, analyzing publications from Scopus and WoS (Web of Science) between 1 January 2020 and 31 December 2024. By synthesizing recent developments, this review provides valuable insights for researchers, highlighting the current trends, challenges and future research directions, in the context of achieving the Sustainable Development Goals. Full article

The role of green and low-carbon energy (gLE) resources in realizing the envisaged future decarbonized energy generation and supply cannot be overemphasized. The world has witnessed growing attention to the application of green energy (gE) sources such as solar, wind, hydro, geothermal, and biomass (energy crops, biogas, biodiesel, etc.). There is also the existence of low-carbon energy (LE) resources such as power-to-X, power-to-fuel, power-to-gas, e-fuel, waste-to-energy, etc., which possess huge potential for delivering sustainable energy, thus facilitating a pathway for achieving the desired environmental sustainability. In addition, the evolution of the cyber-physical power systems and the need for strengthening capacity in advanced energy materials are among the key factors that drive the deployment of gLE technologies around the world. This paper, therefore, presents the recent global developments in gLE resources, including the trends in their deployments for different applications in commercial premises. The study introduces different conceptual technical models and configurations of energy systems; the potential of multi-energy generation in a microgrid (m-grd) based on the gLE resources is also explored using the System Advisor Model (SAM) software. The m-grd is being fueled by solar, wind, and fuel cell resources for supplying a commercial load. The quantity of carbon emissions avoided by the m-grd is evaluated compared to a purely conventional m-grd system. The paper presents the cost of energy and the net present cost of the proposed m-grid; it also discusses the relevance of carbon capture and storage and carbon sequestration technologies. The paper provides deeper insights into the understanding of clean and unconventional energy resources. Full article

The paper presents AgriMicro, a modern Farm Management Information System (FMIS) designed to help farmers monitor and optimize corn crops from sowing to harvest, by leveraging cloud technologies and machine learning algorithms. The platform is built on a modular architecture composed of multiple components implemented through microservices such as the weather and soil service, recommendation and alert engine, field service, and crop service—which continuously communicate to centralize field data and provide real-time insights. Through the ongoing exchange of data between these services, different information pieces about soil conditions, crop health, and agricultural operations are processed and analyzed, resulting in predictions of crop evolution and practical recommendations for future interventions (e.g., fertilization or irrigation). This integrated FMIS transforms collected data into concrete actions, supporting farmers and agricultural consultants in making informed decisions, improving field productivity, and ensuring more efficient resource use. Its microservice-based architecture provides scalability, modularity, and straightforward integration with other information systems. The objectives of this study are threefold. First, to specify and design a modular FMIS architecture based on microservices and cloud computing, ensuring scalability, interoperability and adaptability to different farm contexts. Second, to prototype and integrate initial components and Internet of Things (IoT)-based data collection with machine learning models, specifically Random Forest and XGBoost, to provide maize yield forecasting as a proof of concept. Model performance was evaluated using standard predictive accuracy metrics, including the coefficient of determination (R²) and the root mean square error (RMSE), confirming the reliability of the forecasting pipeline and validated against official harvest data (average maize yield) from the Romanian National Institute of Statistics (INS) for 2024. These results confirm the reliability of the forecasting pipeline under controlled conditions; however, in real-world practice, broader regional and inter-annual variability typically results in considerably higher errors, often on the order of 10–20%. Third, to present a Romania based case study which illustrates the end-to-end workflow and outlines an implementation roadmap toward full deployment. As this is a design-oriented study currently under development, several services remain at the planning or early prototyping stage, and comprehensive system level benchmarks are deferred to future work. Full article

As fluvial deposition features, river islands originate from persistently exposed sandbars. Their morphological evolution responds to hydrological dynamics, sediment budgets, and human modifications of river systems. This study conducts a quantitative analysis of the spatiotemporal evolution of four river islands in China’s Yangtze River Estuary (YRE), utilizing multitemporal Landsat imagery (MSS, TM, ETM+, and OLI) at five-year intervals from 1974 to 2024. This analysis employed thresholding, binarization, image registration, cropping, and cluster analysis. Hydrological data (runoff and sediment flux) from Datong Station were concurrently evaluated to explore the driving factors of evolution. The findings suggested the following: (1) MSS/TM/ETM+/OLI images were effective for accurately extracting river island information, and the results were consistent with the accuracy verification. (2) The cumulative area and growth rate of the river islands have exhibited an upward trend over time, with Jiuduansha growing the fastest. (3) Runoff and sediment discharge are the primary natural controls on morphological evolution, with a weak positive correlation (R = 0.293) and a strong negative correlation (R = −0.915) with the area of river islands, respectively. Anthropogenic drivers such as land reclamation, sediment enhancement projects, and the Three Gorges Dam are equally critical. Full article

rDNA is abundant in various organisms, typically expressed as conserved tandem repeats. It plays a crucial role in ribosome synthesis, gene transcription, and expression, and it affects the occurrence of diseases in both animals and plants, aging, protein synthesis, genomic stability, and genome evolution across a wide range of organisms. Among the different types of rDNA, 35S rDNA (also referred to as 45S rDNA) and 5S rDNA are particularly important in plant research. The use of 35S rDNA and 5S rDNA as probes has enabled the study of chromosomal composition, revealing species characteristics that are valuable for crop breeding, evolutionary biology, systematics, and other fields. This review focuses on the application of 35S rDNA and 5S rDNA and discusses research findings on sugarcane and its related germplasm that have been obtained through fluorescence in situ hybridization. This information has provided a foundation for understanding the genetic relationships, genetics, breeding, and evolutionary classification of sugarcane. Full article

Sweetpotato is a key staple crop in tropical and subtropical regions. Its vegetative propagation makes it a persistent reservoir, facilitating the emergence and spread of complex infections. Understanding its virome is crucial for disease management and food security. We investigated the sweetpotato virome in Burkina Faso using rolling circle amplification and Oxford Nanopore sequencing. Eight symptomatic leaf samples, previously undiagnosed using conventional methods, were analysed. Bioinformatic pipelines were employed followed by phylogenetic comparisons. Two viruses known to infect sweetpotato, namely sweet potato leaf curl virus (SPLCV) and sweet potato leaf curl deltasatellite 3 (SPLCD3), were consistently detected in all samples. Additionally, pepper yellow vein Mali virus (PepYVMV), cotton leaf curl Gezira alphasatellite (CLCuGeA) and cotton leaf curl Gezira betasatellite (CLCuGeB) were identified for the first time in this crop. Phylogenetic analysis confirmed their genetic proximity to isolates from tomato, okra and pepper. Their co-occurrence with SPLCV and SPLCD3 indicates a complex viral landscape that could influence disease severity. This study highlights the underestimated role of sweetpotato as a viral reservoir, influencing virus evolution and transmission. Further studies should assess their pathogenicity, co-infection dynamics and vector-mediated transmission to improve crop productivity. Full article

Understanding the evolutionary and geographic trajectories of crop wild relatives is vital for enhancing agro-biodiversity and advancing climate-resilient agriculture. This study focuses on ten Vicia L. taxa—comprising five species, four varieties, and one subspecies—of significant agricultural importance in Türkiye. An integrative molecular framework was applied, incorporating nuclear ITS sequence data, ITS2 secondary structure modeling, phylogenetic network analysis, and time-calibrated biogeographic reconstruction. This approach revealed well-supported clades, conserved secondary structural elements, and signatures of reticulate evolution, particularly within the Vicia sativa L. and V. villosa Roth. complexes, where high genetic similarity suggests recent divergence and possible hybridization. Anatolia was identified as both a center of origin and a dispersal corridor, with divergence events estimated to have occurred during the Late Miocene–Pliocene epochs. Inferred migration routes extended toward the Balkans, the Caucasus, and Central Asia, corresponding to paleoenvironmental events such as the uplift of the Anatolian Plateau and the Messinian Salinity Crisis. Phylogeographic patterns indicated genetic affiliations between Turkish taxa and drought-adapted Irano-Turanian lineages, offering valuable potential for climate-resilient breeding strategies. The results establish a molecularly informed foundation for conservation and varietal development, supporting sustainability-oriented innovation in forage crop systems and contributing to regional food security. Full article

Growth regulatory factors (GRFs) and growth-regulating interacting factors (GIFs) play significant roles in plant growth, development, and environmental stress responses. Previous studies have reported the functions of GRF and GIF genes in model plants such as Arabidopsis and rice. Nevertheless, the GRF and GIF genes remained unexplored in cassava. Cassava (Manihot esculenta Crantz) is an important tropical economic crop. Its starchy storage roots serve as a major source of food and industrial raw materials, while its protein-rich leaves are widely consumed as leafy vegetables in Africa and other regions, offering high nutritional value and significant horticultural potential. This study identified 28 MeGRFs distributed on 13 chromosomes and 5 MeGIFs on 4 chromosomes through bioinformatic analysis and expression profiling. Promoter analysis uncovered cis-acting elements associated with growth, hormone signaling, and biotic stress responses. Under different tissues and biotic (e.g., cassava bacterial blight, CBB) and abiotic (e.g., drought, low temperature) stress conditions, GRF and GIF genes exhibited differential expression patterns. Real-time quantitative PCR analysis showed a significant expression for 11 MeGRFs and 3 MeGIFs under the Xanthomonas phaseoli pv. manihotis (Xpm) treatment. VIGS functional validation demonstrated that MeGRF28 and MeGIF4 could enhance cassava resistance to bacterial blight, and protein–protein interaction network analysis suggested that they may form a core GRF-GIF complex. This study provides a theoretical basis for understanding the functional evolution of the GRF and GIF gene families in cassava and their roles in horticultural trait development and stress resistance mechanisms. Full article

Southern tomato virus (STV) or Amalgavirus lycopersici is a persistent virus impacting tomato crops globally. This study identified new STV isolates from Oklahoma and analyzed their evolutionary relationship to global STV isolates. Phylogenetic analyses (complete genomes or individual genes) grouped STV isolates into two distinct clades, independent of geographic origin or host. Notably, Oklahoma isolates formed a separate cluster from previously reported isolates in the United States of America (USA). Coalescent analysis suggested the most recent common ancestor of STV fusion protein emerged around 135 years ago. Genetic diversity among STV isolates was low, with slightly more variability in the RNA-dependent RNA polymerase (RdRp) gene than the p42 gene. Both genes showed strong purifying selection. No recombination events were detected across complete genomes. Structure analysis revealed that the p42 protein, particularly its C-terminal region, displayed higher disorder, indicating a possible role in host interactions and viral adaptability. These findings deepen our understanding of STV’s evolution and highlight the need for ongoing surveillance and broader genomic sampling. Full article

Mobile genetic elements such as integrons are key drivers of microbial evolution, enabling rapid adaptation to environmental pressures through the acquisition and rearrangement of gene cassettes. In this study, we explored the structural diversity and synteny of class 1 integrons (intI1) across a set of agroecosystem-related environments, including digestate, compost, and rhizosphere soils from wheat crops (Triticum durum and T. aestivum). Our results reveal distinct gene cassette architectures shaped by the origin of the samples: digestate harbored the most diverse and complex arrays, while compost displayed streamlined structures. Rhizosphere soils exhibited intermediate configurations, reflecting a dynamic balance between environmental exposure and host influence. Genes associated with resistance to antibiotics and heavy metals, such as qacEΔ1 and ebrA, were differentially distributed, suggesting site-specific selective pressures. The observed patterns of cassette organization and diversity underscore the role of integron synteny as a molecular fingerprint of microbial adaptation. These findings position class 1 integrons as promising bioindicators of soil health and functional resilience, supporting a One Health approach to sustainable agriculture and microbial risk monitoring. Full article

Seed size is a key trait affecting evolution and agronomic performance by influencing seedling establishment in natural populations and crop yields. The Arabidopsis thaliana Seed Size QTL1 (SSQ1) locus explains 10–15% of the variation in seed size. We report here that the causal gene for this locus is Tetratricopeptide Repeat Protein 2 (TPR2), which encodes a co-chaperone. Expressing TPR2 across ecotypes and genotypes showed consistent dosage effects. Each additional TPR2^Col-0 allele increased seed mass and volume by 10–14% with high reliability in Col-0, Sha, Tsu-1, and tsu2 genetic backgrounds. Reciprocal genetic crosses indicated that this locus acts maternally, consistent with female sporophytic or female gametophytic mutations. To elucidate how TPR2 regulates seed size, the biomass composition of seeds was measured. While oil content remained unchanged, sucrose levels were markedly elevated in TPR2^Col-0 transformant lines and reduced in tpr2 mutants. Interestingly, heterologous expression of TPR2^Col-0 across genetic backgrounds increased seed protein accumulation by 18% on average. Based on these changes in sucrose and protein levels, potential modes of action for TPR2 are discussed. Full article

Rapeseed (Brassica napus) is a globally important oilseed crop whose yield and quality are frequently limited by weed competition. In recent years, there have been significant advances in our understanding of herbicide-resistance mechanisms in rapeseed and in the development of herbicide-resistant rapeseed germplasm. Here, we summarize the molecular mechanisms of resistance to three herbicides: glyphosate, glufosinate, and acetolactate synthase (ALS) inhibitors. We discuss progress in the identification of new resistance genes and the development of herbicide-resistant rapeseed germplasm, from the initial identification of natural mutants to artificial mutagenesis screening, introduction of exogenous resistance genes, and gene editing. In addition, we describe how synthetic biology and directed protein evolution will contribute to precision-breeding efforts in the near future. This is the first review to systematically integrate non-target resistance mechanisms and the potential applications of multi-omics and AI technologies for breeding of herbicide-resistant rapeseed, together with strategies for managing the risks associated with gene flow, the evolution of herbicide-resistant weeds, and the occurrence of volunteer plants resulting from deployment of herbicide-resistant rapeseed. By synthesizing current knowledge and future trends, this review provides guidance for safe, effective, and innovative approaches to the sustainable development of herbicide-resistant rapeseed. Full article

As the global digital transformation of agriculture accelerates, the widespread deployment of farming equipment has triggered an exponential surge in agricultural production data. Consequently, traditional cloud computing frameworks face critical challenges: communication latency in the field, the demand for low-power devices, and stringent real-time decision constraints. These bottlenecks collectively exacerbate bandwidth constraints, diminish response efficiency, and introduce data security vulnerabilities. In this context, edge computing offers a promising solution for smart agriculture. By provisioning computing resources to the network periphery and enabling localized processing at data sources adjacent to agricultural machinery, sensors, and crops, edge computing leverages low-latency responses, bandwidth optimization, and distributed computation capabilities. This paper provides a comprehensive survey of the research landscape in agricultural edge computing. We begin by defining its core concepts and highlighting its advantages over cloud computing. Subsequently, anchored in the “terminal sensing-edge intelligence-cloud coordination” architecture, we analyze technological evolution in edge sensing devices, lightweight intelligent algorithms, and cooperative communication mechanisms. Additionally, through precision farming, intelligent agricultural machinery control, and full-chain crop traceability, we demonstrate its efficacy in enhancing real-time agricultural decision-making. Finally, we identify adaptation challenges in complex environments and outline future directions for research and development in this field. Full article

Cotton is a globally strategic crop that plays a crucial role in sustaining national economies and livelihoods. To address the challenges of accurate cotton field extraction in the complex planting environments of Xinjiang’s Alaer reclamation area, a cotton field identification model was developed that integrates multi-source satellite remote sensing data with machine learning methods. Using imagery from Sentinel-2, GF-1, and Landsat 8, we performed feature fusion using principal component, Gram–Schmidt (GS), and neural network techniques. Analyses of spectral, vegetation, and texture features revealed that the GS-fused blue bands of Sentinel-2 and Landsat 8 exhibited optimal performance, with a mean value of 16,725, a standard deviation of 2290, and an information entropy of 8.55. These metrics improved by 10,529, 168, and 0.28, respectively, compared with the original Landsat 8 data. In comparative classification experiments, the endmember-based random forest classifier (RFC) achieved the best traditional classification performance, with a kappa value of 0.963 and an overall accuracy (OA) of 97.22% based on 250 samples, resulting in a cotton-field extraction error of 38.58 km². By enhancing the deep learning model, we proposed a U-Net architecture that incorporated a Convolutional Block Attention Module and Atrous Spatial Pyramid Pooling. Using the GS-fused blue band data, the model achieved significantly improved accuracy, with a kappa coefficient of 0.988 and an OA of 98.56%. This advancement reduced the area estimation error to 25.42 km², representing a 34.1% decrease compared with that of the RFC. Based on the optimal model, we constructed a digital map of continuous cotton cropping from 2021 to 2023, which revealed a consistent decline in cotton acreage within the reclaimed areas. This finding underscores the effectiveness of crop rotation policies in mitigating the adverse effects of large-scale monoculture practices. This study confirms that the synergistic integration of multi-source satellite feature fusion and deep learning significantly improves crop identification accuracy, providing reliable technical support for agricultural policy formulation and sustainable farmland management. Full article