Search Results (1,508)

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation. Full article

In the agricultural domain, the availability of labelled data for disease recognition tasks is often limited due to the cost and expertise required for annotation. In this paper, a novel semi-supervised learning framework named TOM-SSL is proposed for automatic tomato leaf disease recognition using pseudo-labelling. TOM-SSL effectively addresses the challenge of limited labelled data by leveraging a small labelled subset and confidently pseudo-labelled samples from a large pool of unlabelled data to improve classification performance. Utilising only 10% of the labelled data, the proposed framework with a MobileNetV3-Small backbone achieves the best accuracy at 72.51% on the tomato subset of the PlantVillage dataset and 70.87% on the Taiwan tomato leaf disease dataset across 10 disease categories in PlantVillage and 6 in the Taiwan dataset. While achieving recognition performance on par with current state-of-the-art supervised methods, notably, the proposed approach offers a tenfold enhancement in label efficiency. Full article

Among the new strategies for managing diseases in agricultural crops is the application of metallic nanoparticles due to their ability to inhibit the development of phytopathogenic microorganisms and to induce plant defense responses. Therefore, this research evaluated the effects of silver (AgNPs), zinc oxide (ZnONPs), and silicon dioxide (SiO₂NPs) nanoparticles on symptom progression and physiological parameters in two pathosystems: Pseudomonas syringae pv. tomato (Psto) in tomato (pathosystem one, culturable pathogen) and Candidatus Liberibacter solanacearum (CaLso) in pepper plants (pathosystem two, non-culturable pathogen). For in vitro pathosystem one assays, SiO₂NPs did not inhibit Psto growth. The minimum inhibitory concentration (MIC) was 31.67 ppm for AgNPs and 194.3 ppm for ZnONPs. Furthermore, the minimum lethal concentration (MLC) for AgNPs was 100 ppm, while for ZnONPs, it was 1000 ppm. For in planta assays, ZnONPs, AgNPs, and SiO₂NPs reduced the number of lesions per leaf, but only ZnONPs significantly decreased the severity. Regarding pathosystem two, AgNPs, ZnONPs, and SiO₂NPs application delayed symptom progression. However, only AgNPs significantly reduced severity percentage. Moreover, treatments with AgNPs and SiO₂NPs increased the plant height and dry weight compared to the results for the control. Full article

Barley leaf stripe, caused by Pyrenophora graminea (Pg), significantly reduces yields across various regions globally. Understanding the resistance mechanisms of barley to Pg is crucial for advancing disease resistance breeding efforts. In this study, two barley genotypes—highly susceptible Alexis and immune Ganpi2—were inoculated with the highly pathogenic Pg isolate QWC for 7, 14, and 18 days. The number of differentially expressed genes (DEGs) in Alexis was 1350, 1898, and 2055 at 7, 14, and 18 days, respectively, while Ganpi2 exhibited 1195, 1682, and 2225 DEGs at the same time points. Gene expression pattern analysis revealed that Alexis responded more slowly to Pg infection compared to Ganpi2. A comparative analysis identified 457 DEGs associated with Ganpi2’s immunity to Pg. Functional enrichment of these DEGs highlighted the involvement of genes related to plant-pathogen interactions and kinase activity in Pg immunity. Additionally, 20 resistance genes and 24 transcription factor genes were predicted from the 457 DEGs. Twelve candidate genes were selected for qRT-PCR verification, and the results showed that the transcriptomic data was reliable. We conducted cloning of the candidate Pg resistance gene HvLRR_8-1 by the barley cultivar Ganpi2, and the sequence analysis confirmed that the HvLRR_8-1 gene contains seven leucine-rich repeat (LRR) domains and an S_TKc domain. Subcellular localization in tobacco indicates that the HvLRR_8-1 is localized on the cell membrane. Through the functional analysis using virus-induced gene silencing, it was demonstrated that HvLRR_8-1 plays a critical role in regulating barley resistance to Pg. This study represents the first comparative transcriptome analysis of barley varieties with differing responses to Pg infection, providing that HvLRR_8-1 represents a promising candidate gene for improving durable resistance against Pg in cultivated barley. Full article

Although favorable effects of Selenium nanoparticles (SeNPs or nSe) in tomato have been reported, research has concentrated on stress alleviation and disease management. From the above it is noticeable that the effect of NPs varies greatly depending on the model plant, nanoparticle (concentration, size, shape), and application (foliar or drenching). For this reason, the objective of this study was to investigate the impact of biostimulating tomato plants under no stressor conditions (Solanum lycopersicum cv. ‘Pomodoro’ L.) with SeNPs on morpho-physiological and fruit quality parameters. Three doses of Selenium nanoparticles (5, 15, and 30 mg L⁻¹), and a control were applied via a foliar application after transplanting. The results indicate that a 5 mg L⁻¹ SeNP treatment improved the growth and yield of the tomato, with the exception of the root length and leaf weight. Moreover, all doses modified the evaluated physiology, bioactive compounds, and fruit quality parameters. This research helped in understanding the SeNPs’ effect on tomato plants in greenhouses under a no stressor condition. Full article

Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt in eggplant leaves caused by Verticillium dahliae by integrating multispectral imaging with machine learning and deep learning techniques. Multispectral and chlorophyll fluorescence images were collected from leaves of the inbred eggplant line 11-435, including data on image texture, spectral reflectance, and chlorophyll fluorescence. Subsequently, we established a multispectral data model, fusion information model, and multispectral image–information fusion model. The multispectral image–information fusion model, integrated with a two-dimensional convolutional neural network (2D-CNN), demonstrated optimal performance in classifying early-stage Verticillium wilt infection, achieving a test accuracy of 99.37%. Additionally, transfer learning enabled us to diagnose early leaf wilt in another eggplant variety, the inbred line 14-345, with an accuracy of 84.54 ± 1.82%. Compared to traditional methods that rely on visible symptom observation and typically require about 10 days to confirm infection, this study achieved early detection of Verticillium wilt as soon as the third day post-inoculation. These findings underscore the potential of the fusion model as a valuable tool for the early detection of pre-symptomatic states in infected plants, thereby offering theoretical support for in-field detection of eggplant health. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

The rapid evolution of pathogens and the limited genetic diversity of hosts are two major factors contributing to the plant pathogenic phenomenon known as the loss of disease resistance in maize (Zea mays L.). It has emerged as a significant biological stressor threatening the global food supplies and security. Based on previous cross-species homologous gene screening assays conducted in the laboratory, this study identified the maize disease-resistance candidate gene ZmNLR-7 to investigate the maize immune regulation mechanism against Bipolaris maydis. Subcellular localization assays confirmed that the ZmNLR-7 protein is localized in the plasma membrane and nucleus, and phylogenetic analysis revealed that it contains a conserved NB-ARC domain. Analysis of tissue expression patterns revealed that ZmNLR-7 was expressed in all maize tissues, with the highest expression level (5.11 times) exhibited in the leaves, and that its transcription level peaked at 11.92 times 48 h post Bipolaris maydis infection. Upon inoculating the ZmNLR-7 EMS mutants with Bipolaris maydis, the disease index was increased to 33.89 and 43.33, respectively, and the lesion expansion rate was higher than that in the wild type, indicating enhanced susceptibility to southern corn leaf blight. Physiological index measurements revealed a disturbance of ROS metabolism in ZmNLR-7 EMS mutants, with SOD activity decreased by approximately 30% and 55%, and POD activity decreased by 18% and 22%. Moreover, H₂O₂ content decreased, while lipid peroxide MDA accumulation increased. Transcriptomic analysis revealed a significant inhibition of the expression of the key genes NPR1 and ACS6 in the SA/ET signaling pathway and a decrease in the expression of disease-related genes ERF1 and PR1. This study established a new paradigm for the study of NLR protein-mediated plant immune mechanisms and provided target genes for molecular breeding of disease resistance in maize. Overall, these findings provide the first evidence that ZmNLR-7 confers resistance to southern corn leaf blight in maize by synergistically regulating ROS homeostasis, SA/ET signal transduction, and downstream defense gene expression networks. Full article

Wheat leaf rust (Lr), caused by Puccinia triticina Eriks. (Pt), is one of the most important diseases affecting wheat production worldwide. Using resistant wheat cultivars is the most economic and environmentally friendly way to control leaf rust. The Chinese wheat cultivar Xinong1163-4 has shown good resistance to Lr in field trials. To identify the genetic basis of Lr resistance in Xinong1163-4, 195 recombinant inbred lines (RILs) from the Xinong1163-4/Thatcher cross were phenotyped for Lr severity in three environments: the 2017/2018, 2018/2019, and 2019/2020 growing seasons in Baoding, Hebei Province. Bulked segregant analysis and simple sequence repeat markers were then used to identify the quantitative trait loci (QTLs) for Lr adult plant resistance (APR) in the population. As a result, six QTLs were detected, designated as QLr.hbau-1BL.1, QLr.hbau-1BL.2, and QLr.hbau-1BL.3. These QTLs were predicted to be novel. QLr.hbau-4BL, QLr.hbau-4BL.1, and QLr.hbau-3A were identified at similar physical positions to previously reported QTLs. Based on chromosome positions and molecular marker testing, QLr.hbau-1BL.3 shares similar flanking markers with Lr46. Lr46 is a non-race-specific APR gene for leaf rust, stripe rust, and powdery mildew. Similarly, QLr.hebau-4BL showed resistance to multiple diseases, including leaf rust, stripe rust, Fusarium head blight, and powdery mildew. The QTLs identified in this study, as well as their closely linked markers, can potentially be used for marker-assisted selection in wheat breeding. Full article

Downy mildew, caused by Plasmopara viticola, is a major threat to grapevine (Vitis vinifera) cultivation in humid climates. Restrictions on synthetic pesticides and inconsistent efficacy of current biocontrol agents, especially under rainy conditions, complicate disease management. This study evaluated the potential of compost tea to suppress downy mildew in a two-year field experiment (2023 and 2024), combined with reduced synthetic fungicide applications. The study design compared two phytosanitary management strategies on a commercial vineyard: a conventional fungicide against a compost tea strategy supplemented with two cymoxanil applications. The experiment set up had three replicated blocks, each consisting of 100 plants for a total of 600 plants. Mechanistic insights were provided through controlled laboratory experiments involving pre- and post-infection leaf assays, vineyard bacteriome profiling, via 16S rRNA gene sequencing for bacterial communities, across vineyard compartments, i.e., bulk soil, rhizosphere, and phyllosphere, and grapevine metabolomic analysis by GC-MS analysis. Field trials demonstrated that compost tea combined with two fungicide applications effectively reduced disease severity, notably outperforming the fungicide alone in the particularly rainy year of 2023. Bacteriome analysis revealed that compost tea treatment enriched beneficial bacterial genera, including Pseudomonas, Sphingomonas, Enterobacter, Massilia, and Bacillus, known for their growth-promoting and biocontrol activity in the rhizosphere and phyllosphere. Laboratory assays on detached leaves further showed that compost tea alone could suppress both infection and sporulation of P. viticola. Metabolomic analysis highlighted the accumulation of compounds such as tartaric and shikimic acids in compost tea treated leaves, suggesting a potential role in induced resistance. The findings indicate that applying compost tea with reduced fungicide treatments represents a promising and sustainable strategy for managing grapevine downy mildew, even in challenging climates. Full article

Tomato leaf curl New Delhi virus (ToLCNDV) is among the most prevalent and widely distributed begomovirus infecting chili pepper (Capsicum annuum) and tomato in the Indian subcontinent. In this study, a guide RNA (gRNA) sequence-CRISPR-Cas9 approach was used to target and cleave two essential coding regions in the begomovirus genome. The gRNAs were designed to target conserved regions of the ToLCNDV replication-associated protein (rep) gene or ORF AC1, and/or the coat protein (cp) gene or AV1 ORF, respectively. Based on an alignment of 346 representative ToLCNDV genome sequences, all predicted single nucleotide polymorphisms off-target sites were identified and eliminated as potential gRNA targets. Based on the remaining genome regions, four candidate gRNAs were designed and used to build gRNA-Cas9 duplexed constructs, e.g., containing two gRNAs cloned in tandem, in different combinations (1–4). Two contained two gRNAs that targeted the coat protein gene (cp; AV1 ORF), while the other two constructs targeted both the cp and replication-associated protein gene (rep; AC1 ORF). These constructs were evaluated for the potential to suppress ToLCNDV infection in Nicotiana benthamiana plants in a transient expression-transfection assay. Among the plants inoculated with the duplexed gRNA construct designed to cleave ToLCNDV-AV1 or AC1-specific nucleotides, the construct designed to target both the cp (293–993 nt) and rep (1561–2324) showed the greatest reduction in virus accumulation, based on real-time quantitative PCR amplification, and attenuated disease symptoms, compared to plants inoculated with the DNA-A component alone or mock-inoculated, e.g., with buffer. The results demonstrate the potential for gRNA-mediated suppression of ToLCNDV infection in plants by targeting at least two viral coding regions, underscoring the great potential of CRISPR-Cas-mediated abatement of begomovirus infection in numerous crop species. Full article

Background: Alcohol-associated liver disease (ALD) is a primary global health concern, exacerbated by oxidative stress, inflammation, and gut barrier dysfunction. Conventional phytocompounds exhibit hepatoprotective potential but are hindered by low bioavailability. This study aimed to evaluate the hepatoprotective and gut-barrier-restorative effects of green-synthesized silver nanoparticles (AgNPs) derived from Enicostemma littorale, a medicinal plant known for its antioxidant and anti-inflammatory properties. Methods: AgNPs were synthesized using aqueous leaf extract of E. littorale and characterized using UV-Vis, XRD, FTIR, DLS, and SEM. HepG2 (liver) and Caco-2 (colon) cells were exposed to 0.2 M ethanol, AgNPs (1–100 µg/mL), or both, to simulate ethanol-induced toxicity. A range of in vitro assays was performed to assess cell viability, oxidative stress (H₂DCFDA), nuclear and morphological integrity (DAPI and AO/EtBr staining), lipid accumulation (Oil Red O), and gene expression of pro- and anti-inflammatory, antioxidant, and tight-junction markers using RT-qPCR. Results: Ethanol exposure significantly increased ROS, lipid accumulation, and the expression of inflammatory genes, while decreasing antioxidant enzymes and tight-junction proteins. Green AgNPs at lower concentrations (1 and 10 µg/mL) restored cell viability, reduced ROS levels, preserved nuclear morphology, and downregulated CYP2E1 and SREBP expression. Notably, AgNPs improved the expression of Nrf2, HO-1, ZO-1, and IL-10, and reduced TNF-α and IL-6 expression in both cell lines, indicating protective effects on both liver and intestinal cells. Conclusions: Green-synthesized AgNPs from E. littorale exhibit potent hepatoprotective and gut-barrier-restoring effects through antioxidant, anti-inflammatory, and antilipidemic mechanisms. These findings support the therapeutic potential of plant-based nanoparticles in mitigating ethanol-induced gut–liver axis dysfunction. Full article

Lippia multiflora Moldenke is widely used in Angola, on the African continent, and beyond for the treatment of many health conditions such as hypertension, enteritis, colds, gastrointestinal disturbances, stomachaches, jaundice, coughs, fevers, nausea, bronchial inflammation, conjunctivitis, malaria, and venereal diseases. However, there is limited literature about the active compounds linked with the reported biological activities. This study aims to assess the chemical profiles, antioxidant properties, and the cytotoxicity effects of the roots, stem bark, and leaves of L. multiflora. Chemical characterization of the crude extracts was assessed through quantification of total phenolic and flavonoid contents followed by Q exactive plus orbitrap™ ultra-high-performance liquid chromatography-mass spectrometer (UHPLC-MS) screening. The correlation between the extracts and the correlation between the compounds were studied using the multivariate analysis. Principal component analysis (PCA) loading scores and principal component analysis (PCA) biplots and correlation plots were used to connect specific compounds with observed biological activities. The antioxidant activities of the crude extracts were carried out in vitro using DPPH (2,2-diphenyl-1-picrylhydrazyl) free radical scavenging and reducing power assays, while the in vitro toxicology of the crude extracts was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. A total of twenty constituents were characterized and identified using the UHPLC–Q/Orbitrap/MS. The methanol leaf extract showed the highest antioxidant activity in the DPPH free radical scavenging activity (IC₅₀ = 0.559 ± 0.269 μg/mL); however, the stem bark extract had the highest reducing power (IC_0.5 = 0.029 ± 0.026 μg/mL). High phenolic and flavonoid content was found in the dichloromethane leaf extract (32.100 ± 1.780 mg GAE/g) and stem bark extract (624.153 ± 29.442 mg QE/g), respectively. The results show the stem bark, methanol leaf, and dichloromethane leaf extracts were well-tolerated by the Vero cell line at concentrations up to 50 µg/mL. However, at 100 µg/mL onward, some toxicity was observed in the root, methanol leaf, and dichloromethane leaf extracts. The UHPLC–Q/Orbitrap/MS profiles showed the presence of terpenoids (n = 5), flavonoids (n = 5), phenols (n = 4), alkaloids (n = 3), coumarins (n = 1), fatty acids (n = 1), and organic acids (n = 1). According to several studies, these secondary metabolites have been reported and proven to be the most abundant for antioxidant potential. The identified flavonoids (catechin, quercitrin, and (−)-epigallocatechin) and phenolic compound (6-gingerol) can significantly contribute to the antioxidant properties of different plant parts of L. multiflora. The research findings obtained in this study provide a complete phytochemical profile of various parts of L. multiflora that are responsible for the antioxidant activity using UHPLC–Q/Orbitrap/MS analysis. Furthermore, the results obtained in this study contribute to the scientific information or data on the therapeutic properties of Lippia multiflora and provide a basis for further assessment of its potential as a natural remedy. Full article

In the recognition of fruit leaf diseases, image recognition technology based on deep learning has received increasing attention. However, deep learning models often perform poorly in complex backgrounds, and in some cases, they even outperform traditional algorithms. To address this issue, this paper proposes a Frequency-Adaptive Attention (FA-attention) mechanism that leverages the significance of frequency-domain features in fruit leaf disease regions. By enhancing the processing of frequency domain features, the recognition performance in complex backgrounds is improved. Specifically, FA-attention combines Fourier transform with the attention mechanism to extract frequency domain features as key features. Then, this mechanism is integrated with the Unet model to obtain feature maps strongly related to frequency domain features. These feature maps are fused with multi-scale convolutional feature maps and then used for classification. Experiments were conducted on the Plant Village (PV) dataset and the Plant Pathology (PP) dataset with complex backgrounds. The results indicate that the proposed FA-attention mechanism achieves significant effects in learning frequency domain features. Our model achieves a recognition accuracy of 99.91% on the PV dataset and 89.59% on the PP dataset. At the same time, the convergence speed is significantly improved, reaching 94% accuracy with only 20 epochs, demonstrating the effectiveness of this method. Compared with classical models and state-of-the-art (SOTA) models, our model performs better on complex background datasets, demonstrating strong generalization capabilities. Full article

Rootstocks are vital in cucumber production. Although figleaf gourd (Cucurbita ficifolia) is among the species used, its application remains limited due to the perception that white-seeded pumpkin (C. maxima × C. moschata) offers superior commercial traits. This perception is partly due to the insufficient collection and evaluation of local figleaf gourd germplasm, which has obscured its potential as a rootstock. Based on prior screening, four wild figleaf gourd genotypes from Yunnan Province were selected and compared with seven commercial white-seeded pumpkin rootstocks. Scions grafted onto figleaf gourd exhibited vegetative growth (stem diameter, plant height, and leaf area) and fruit morphology (length, diameter, biomass, and surface bloom) comparable to the top-performing white-seeded pumpkin genotypes. Fruits from figleaf gourd rootstocks also displayed comparable or significantly higher nutritional quality, including vitamin C, total soluble solids, soluble sugars, and proteins. Notably, figleaf gourd itself showed significantly greater intrinsic resistance to Fusarium wilt than white-seeded pumpkin. When used as a rootstock, it protected the scion from pathogen stress by triggering a stronger antioxidant response (higher SOD and POD activity) and mitigating cellular damage (lower MDA levels and electrolyte leakage). These results provide evidence that these figleaf gourd genotypes are not merely viable alternatives but are high-performing rootstocks, particularly in enhancing nutritional value and providing elite disease resistance. Full article