Search Results (223)

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

The introduction of virus resistance genes into traditional tomato varieties offers a strategy to preserve genetic diversity and enhance commercial viability. However, the homozygous presence of these genes has been associated with negative effects on yield and fruit quality. This two-year study evaluated the impact of introducing the Tm-2a, Sw-5 and Ty-1 genes, which are associated with resistance to ToMV, TSWV and TYLCV, respectively, on the agronomic yield, fruit characteristics and metabolic profile of Muchamiel-type cultivars. Four hybrids were obtained by crossing two breeding lines carrying the resistance genes in homozygosis (UMH1139 and UMH1200) with two traditional susceptible varieties (MC1 and MC2). Hybrids matched or exceeded the agronomic performance of their parents. Fruit morphology of the hybrids was similar to traditional parents. The presence of Ty-1 correlated with reduced organic acid concentration, though hybrids exhibited higher levels than the homozygous line, UMH1200. No negative effects on soluble sugars or secondary metabolites were observed. Genotypes carrying resistance genes, breeding lines and hybrids exhibited higher flavonoid contents, suggesting a potential role in virus response. Hybrids maintained or improved the bioactive profile of traditional varieties. These findings support the development of Muchamiel-type hybrids that combine the presence of virus resistance genes in heterozygosity with the desirable traits of traditional tomatoes. Full article

Accurate and early detection of plant diseases is essential for effective management and the advancement of sustainable smart agriculture. However, building large annotated datasets for disease classification is often costly and time-consuming, requiring expert input. To address this challenge, this study explores the integration of few-shot learning with hyperspectral imaging to detect four major fungal diseases in tomato plants: Alternaria alternata, Alternaria solani, Botrytis cinerea, and Fusarium oxysporum. Following inoculation, hyperspectral images were captured every other day from Day 1 to Day 7 post inoculation. The proposed hybrid method includes three main steps: (1) preprocessing of hyperspectral image cubes, (2) deep feature extraction using the EfficientNet model, and (3) classification using Manhattan distance within a few-shot learning framework. This combination leverages the strengths of both spectral imaging and deep learning for robust detection with minimal data. The few-shot learning approach achieved high detection accuracies of 85.73%, 80.05%, 90.33%, and 82.09% for A. alternata, A. solani, B. cinerea, and F. oxysporum, respectively, based on data collected on Day 7 post inoculation using only three training images per class. Accuracy improved over time, reflecting the progressive nature of symptom development and the model’s adaptability with limited data. Notably, A. alternata and B. cinerea were reliably detected by Day 3, while A. solani and F. oxysporum reached dependable detection levels by Day 5. Routine visual assessments showed that A. alternata and B. cinerea developed visible symptoms by Day 5, whereas A. solani and F. oxysporum remained asymptomatic until Day 7. The model’s ability to detect infections up to two days before visual symptoms emerged highlights its value for pre-symptomatic diagnosis. These findings support the use of few-shot learning and hyperspectral imaging for early, accurate disease detection, offering a practical solution for precision agriculture and timely intervention. Full article

Root-knot nematodes, Meloidogyne spp., are widespread phytoparasites that cause a significant reduction in the yield of tomato Solanum lycopersicum. In the Russian Federation, where the use of chemical nematicides is limited due to environmental and toxicological risks, the cultivation of resistant varieties and hybrids remains the most effective and environmentally safe method to control Meloidogyne. In the course of this study, the resistance screening of 20 tomato varieties/hybrids and 21 mutant lines from the collection of the FSBSI FRCBPP to M. hapla was carried out using a comprehensive approach that included morphological and biochemical analysis methods. Resistance was assessed by calculating the gall formation index, the degree of root system damage, and biochemical parameters of fruits—vitamin C content and titratable acidity. In addition, molecular screening was carried out using the SCAR marker Mi23 to identify the Mi-1.2 gene, known as a key factor in resistance to a number of Meloidogyne spp. Although Mi-1.2 is not typically associated with resistance to M. hapla, all genotypes carrying this gene showed phenotypic resistance. This unexpected correlation suggests the possible involvement of Mi-associated or parallel mechanisms and highlights the need for further investigation into noncanonical resistance pathways. It was found that when susceptible genotypes were infected with M. hapla, there was a tendency for the vitamin C content to decrease, while resistant lines retained values close to the control. The presence of the Mi-1.2 gene was confirmed in 9.5% of samples. However, the phenotypic resistance of some lines, such as Volgogradets, which do not contain a marker for the Mi-1.2 gene, indicates a polygenic nature of resistance, alternative genetic mechanisms, or the possible influence of epigenetic mechanisms. The obtained data highlight the potential of using the identified resistant genotypes in breeding programs and the need for further studies of the molecular mechanisms of resistance, including the search for new markers specific to M. hapla, to develop effective strategies for tomato protection in sustainable agriculture. Full article

This study aims to comparatively analyze and evaluate the postharvest quality of tomatoes, and to further screen and utilize the excellent tomato germplasm resources. Correlation analysis, principal component analysis (PCA), and cluster analysis were performed on 18 appearance and nutritional quality indicators of 57 tomato F1 hybrids (labeled accession 1# to 57#). The results show that the variation coefficients of the tested quality indicators among tomato accessions ranged from 3.77% to 42.92%. Among them, 11 indicators had variation coefficients greater than 10%. The soluble protein content had the highest variation coefficient. Six principal components were extracted through PCA, accounting for 78.696% of the variability. The appearance indicators (size, single fruit weight, and a* value) and soluble solid content played key roles in tomato quality evaluation. According to the calculated comprehensive scores, the top 10 accessions with superior overall quality were selected from the tested tomato accessions. Cluster analysis divided the 57 tomato accessions into two major groups and seven subgroups. Notably, accession 6# showed the best flavor and nutritional quality, which could be a focus for future tomato breeding. These results provide a theoretical basis for comprehensive evaluation of tomato and quality improvement in tomato breeding. Full article

Chaperonin 60 proteins plays an important role in plant growth and development as well as the response to abiotic stress. As part of the protein homeostasis system, molecular chaperones have attracted increasing attention in recent years due to their involvement in the folding and assembly of key proteins in photosynthesis. However, little is known about the function of maize chaperonin 60 protein. In the study, a gene encoding the chaperonin 60 proteins was cloned from the maize inbred line B73, and named ZmCpn60-3. The gene was 1, 818 bp in length and encoded a protein consisting of 605 amino acids. Phylogenetic analysis showed that ZmCpn60-3 had high similarity with OsCPN60-1, belonging to the β subunits of the chloroplast chaperonin 60 protein family, and it was predicted to be localized in chloroplasts. The ZmCpn60-3 was highly expressed in the stems and tassels of maize, and could be induced by exogenous plant hormones, mycotoxins, and pathogens; Overexpression of ZmCpn60-3 in Arabidopsis improved the resistance to Pst DC3000 by inducing the hypersensitive response and the expression of SA signaling-related genes, and the H₂O₂ and the SA contents of ZmCpn60-3-overexpressing Arabidopsis infected with Pst DC3000 accumulated significantly when compared to the wild-type controls. Experimental data demonstrate that flg22 treatment significantly upregulated transcriptional levels of the PR1 defense gene in ZmCpn60-3-transfected maize protoplasts. Notably, the enhanced resistance phenotype against Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) in ZmCpn60-3-overexpressing transgenic lines was specifically abolished by pretreatment with ABT, a salicylic acid (SA) biosynthetic inhibitor. Our integrated findings reveal that this chaperonin protein orchestrates plant immune responses through a dual mechanism: triggering a reactive oxygen species (ROS) burst while simultaneously activating SA-mediated signaling cascades, thereby synergistically enhancing host disease resistance. Additionally, yeast two-hybrid assay preliminary data indicated that ZmCpn60-3 might bind to ZmbHLH118 and ZmBURP7, indicating ZmCpn60-3 might be involved in plant abiotic responses. The results provided a reference for comprehensively understanding the resistance mechanism of ZmCpn60-3 in plant responses to abiotic or biotic stress. Full article

The GLABROUS1 enhancer-binding protein (GeBP) gene family, a plant-specific class of transcriptional regulators, is involved in multiple biological processes, including the formation of trichomes, plant growth, and environmental adaptation. However, the functional characterization of SlGeBP genes in tomato remains poor, particularly regarding their roles in regulating developmental processes and stress response mechanisms. In this study, 11 SlGeBP family members were identified from the tomato genome and 97 GeBP proteins from six species were classified into three groups. A wide range of elements linked to phytohormone, stress, and plant development were presented on the promoter sequences. Gene expression profile analysis revealed a comprehensive expression during the vegetative and immature fruit development stages. Analysis of the expression level under nine hormones and seven stresses can help us to understand the responsiveness of SlGeBP genes associated with hormone induction and stress tolerance. Subcellular localization analysis exhibited that SlGeBP1 and SlGeBP5 were localized in the nucleus, and the yeast two-hybrid assay confirmed that SlGeBP1 could interact with SlGeBP5. This study will help us to understand the potential function of the SlGeBP family and may establish a basis for further research on phytohormone signaling and stress resistance. Full article

Dendrobium officinale is a traditional and valuable medicinal herb, with extensive research conducted on its polysaccharides, alkaloids, and other components, yet studies on anthocyanins remain limited. In this study, we analyzed the expression levels of GST family genes in green and purplish D. officinale and found that DoGSTF11 is highly expressed in the purplish variety. DoGSTF11 is localized to the nucleus and cell membrane but lacks transcriptional activation activity. Overexpression of DoGSTF11 in tomato enhances anthocyanin accumulation, suggesting a role in anthocyanin sequestration or transport. Yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays further revealed that DoGSTF11 interacts with DoGST31, while DoIDD12 and DoNF-YB3 are potential transcriptional regulators based on promoter-binding assays and expression correlation. In conclusion, our study demonstrates that DoGST11 positively regulates anthocyanin accumulation in D. officinale. These findings provide valuable insights into the metabolic engineering of flavonoids in D. officinale. Full article

Tomato spotted wilt virus (TSWV) is a highly destructive plant pathogen and transmitted by several thrips including the western flower thrips, Frankliniella occidentalis. A structural N protein encoded in the viral genome represents the nucleocapsid protein by binding to the viral RNA genome. However, it remains unknown how the RNA-binding protein specifically interacts with the viral RNA from host RNAs in the target cells. To study the molecular basis of N function, we produced the protein in Escherichia coli and the resulting purified recombinant protein was used to investigate the protein–RNA interactions. The recombinant N protein migrated on agarose gel to the anode in the electric field due to its high basic isoelectric point. This electrostatic property led N protein to bind to DNA as well as RNA. It also bound to both single-stranded (ssRNA) and double-stranded RNA (dsRNA). However, when the total RNA was extracted from plant tissues collected from TSWV-infected host, the RNA extract using the recombinant N protein was much richer in the TSWV genome compared to that without the protein. To investigate the specificity of N protein to ssRNA, the three-dimensional structure was predicted using the AlphaFold program and showed its trimeric oligomerization with the binding pocket for ssRNA. This was supported by the differential susceptibility of N protein with ssRNA and dsRNA against RNase attack. Furthermore, a thermal shift assay to analyze the RNA and protein interaction showed that ssRNA strongly interacted with N protein compared to dsRNA. In addition, the N gene was expressed along with the multiplication of the viral RNA genome segments from the segment-specific fluorescence in situ hybridization analysis in different tissues during different developmental stages of the virus-infected F. occidentalis. These results suggest that the functional trimeric N proteins bind to the viral RNA to form a basic nucleocapsid structure at a specific virus-replicating compartment within the host cells. Full article

Convolutional neural networks (CNNs) are increasingly applied in crop disease identification, yet most existing techniques are optimized solely for laboratory environments. When confronted with real-world challenges such as diverse disease morphologies, complex backgrounds, and subtle feature variations, these models often exhibit insufficient robustness. To effectively identify fine-grained disease features in complex scenarios while reducing deployment and training costs, this paper proposes a novel network architecture named AHN-YOLO, based on an improved YOLOv11-n framework that demonstrates balanced performance in multi-scale feature processing. The key innovations of AHN-YOLO include (1) the introduction of an ADown module to reduce model parameters; (2) the adoption of a Normalized Wasserstein Distance (NWD) loss function to stabilize small-feature detection; and (3) the proposal of a lightweight hybrid attention mechanism, Light-ES, to enhance focus on disease regions. Compared to the original architecture, AHN-YOLO achieves a 17.1 % reduction in model size. Comparative experiments on a tomato disease detection dataset under real-world complex conditions demonstrate that AHN-YOLO improves accuracy, recall, and mAP-50 by 9.5%, 7.5%, and 9.2%, respectively, indicating a significant enhancement in detection precision. When benchmarked against other lightweight models in the field, AHN-YOLO exhibits superior training efficiency and detection accuracy in complex, dense scenarios, demonstrating clear advantages. Full article

Tomatoes (Solanum lycopersicum) and potatoes (Solanum tuberosum) are vital staple crops. They are prone to diseases from pathogens like Ralstonia and Fusarium, which cause significant agricultural losses. Detecting volatile organic compounds (VOCs) emitted by plants under stress offers a promising approach for advanced monitoring of crop health. This study examines sensing materials for wearable plant sensors targeting VOCs as biomarkers under abiotic and biotic stress. Key questions addressed include the specific VOC emission profiles of potato and tomato cultivars, how materials and sensing mechanisms influence sensor performance, and material considerations for agricultural use. The analysis reveals cultivar-specific VOC profiles under stress, challenging the identification of universal biomarkers for specific diseases. Through a literature review, this study reviews VOC responses to fungi, bacteria, and viruses, and compares non-composite and hybrid chemiresistive and electrochemical sensors based on sensitivity, selectivity, detection limits, response time, robustness, cost-effectiveness, and biocompatibility. A superstructure bridging materials science, plant pathology, AI, data science, and manufacturing is proposed, emphasizing three strategies: sensitivity, flexibility, and sustainability. This study identifies recent research trends that involve developing biodegradable wearable sensors for precision agriculture, leveraging flexible biocompatible materials, multi-parameter monitoring, self-healing properties, 3D-printed designs, advanced nanomaterials, and energy-harvesting technologies. Full article

Tomato (Solanum lycopersicum) is highly susceptible to both high and low temperatures, which threaten its growth, yield, and quality. Ascorbate peroxidase (APX) plays a pivotal role in plant responses to abiotic stresses. In this study, we unveil the positive involvement of heat- and cold-induced SlAPX2 in bolstering tomato resilience to temperature extremes. Knockout of SlAPX2 using the CRISPR/Cas9 technique exacerbated oxidative stress under heat and cold conditions, as evidenced by reduced Fv/Fm and increased electrolyte leakage (REL), malondialdehyde (MDA) content, and hydrogen peroxide (H₂O₂) levels. Furthermore, SlAPX2 expression was modulated by abscisic acid (ABA), and the transcription factor ABF4 in the ABA signaling pathway positively regulated SlAPX2 transcription. Using yeast one-hybrid (Y1H) and dual luciferase (LUC) assays, we found that ABF4 directly bound to the SlAPX2 promoter, thereby activating its transcription. Additionally, silencing of SlABF4 compromised tomato’s tolerance to heat or cold. Collectively, these findings reveal a regulatory module, SlABF4–SlAPX2, that enhances tomato tolerance to temperature extremes by detoxifying excessive reactive oxygen species (ROS). This study advances our understanding of ABA-mediated stress responses and highlights the SlABF4–SlAPX2 module as a promising target for breeding temperature-resilient tomato cultivars. Full article

This study was based on the hypothesis that the hybrid type and its physical–mechanical properties significantly influence the operational efficiency of transplanting systems. Understanding these properties is essential for optimizing the performance of semi-automatic and automatic transplanters. To test this hypothesis, a completely randomized design was implemented to evaluate the physical–mechanical properties of tomato seedlings. A total of 1350 seedlings from three F1 hybrids—Natalie (H1), CID (H2), and Gavilán (H3)—cultivated in central Mexico, were analyzed. The statistical analyses included mean comparisons using Tukey’s test and multiple linear regression to estimate the center of mass (CM). The results indicate that H2 was notable for its total height (${\mathrm{h}}_{\mathrm{t}}$ = 311.76 mm), canopy development in X, Y, and Z axes (170.24 mm, 106.84 mm, and 98.14 mm, respectively), stem diameter (${\mathrm{d}}_{\mathrm{s}}$ = 3.65 mm), total weight (${\mathrm{w}}_{\mathrm{t}}$ = 11.92 g), ${\mathrm{d}}_{\mathrm{e}}$ (78.36 mm) and ${\mathrm{d}}_{\mathrm{p}}$ (233.40 mm) distances, and oscillation period (T = 0.88 s). H1 had the highest stem height (${\mathrm{h}}_{\mathrm{s}}$ = 53.18 mm), ${\mathrm{w}}_{\mathrm{t}}$ = 11.76 g, and root ball (RB) moisture content (MC) (77.36%). H3 had the largest ${\mathrm{d}}_{\mathrm{s}}$ = 3.70 mm, as well as the highest MC in the stem (94.51%) and the remaining foliage (92.92%). Regarding mechanical properties, the average adhesion force (AF) was 4.606 N (H1), 7.470 N (H2), and 3.815 N (H3). The average root ball punching force (RBPF) was 0.36, 0.48, and 0.25 N, respectively. The lowest static friction coefficient (SFC) on a galvanized steel sheet was 0.936. The drop test (DT) revealed an average residual substrate mass of 0.148 g at a height of 500 mm. It can be concluded that the interaction between hybrid type, transplanting age, and MC plays a critical role in the efficient design of semi-automatic and automatic transplanting equipment. This interaction enables process optimization, ensures operational quality, reduces seedling damage, and ultimately enhances and increases the long-term profitability and sustainability of the equipment. Full article

Plant U-box E3 ubiquitin ligases (PUBs) serve as crucial regulators of protein degradation and are fundamentally involved in plant developmental processes and stress response mechanisms. Despite their well-characterized roles in model plant species, the PUB gene family in the hybrid poplar (Populus alba × P. tremula var. glandulosa) remains poorly understood. By conducting a comprehensive genome-wide analysis, we identified 152 PUB genes in poplar and phylogenetically classified them into five distinct clades based on a comparative analysis with Arabidopsis thaliana and tomato PUB homologs. The structural characterization revealed that numerous PagPUB proteins possess additional functional domains, including ARM and WD40 repeats, which are indicative of potential functional diversification. Genomic distribution and synteny analyses demonstrated that the expansion of the PUB gene family predominantly resulted from whole-genome duplication (WGD) events, with evolutionary constraint analyses (Ka/Ks ratios < 1) suggesting strong purifying selection. An examination of the promoter region uncovered an abundance of stress-responsive cis-elements, particularly ABRE and MYB binding sites associated with abiotic stress and hormonal regulation. Transcriptome profiling demonstrated both tissue-specific expression patterns and dynamic regulation under diverse stress conditions, including drought, salinity, temperature extremes, and pathogen infection. Our findings provide the first systematic characterization of the PUB gene family in poplar and establish a valuable framework for elucidating their evolutionary history and functional significance in environmental stress adaptation. Full article

In recent years, China’s vegetable market has faced frequent and drastic price fluctuations due to factors such as supply–demand relationships and climate change, which significantly affect government bodies, farmers, consumers, and other participants in the vegetable industry and supply chain. Traditional forecasting methods demonstrate evident limitations in capturing the nonlinear characteristics and complex volatility patterns of price series, underscoring the necessity of developing high-precision prediction models. This study proposes a hybrid forecasting model integrating variational mode decomposition (VMD), the Fruit Fly Optimization Algorithm (FOA), and a gated recurrent unit (GRU). The model employs VMD for multi-scale decomposition of original price series and utilizes the FOA for adaptive optimization of the GRU’s critical parameters, effectively addressing the challenges of high volatility and nonlinearity in agricultural price forecasting. Empirical analysis conducted on daily price data of six major vegetables, specifically, Chinese cabbage, cucumber, beans, tomato, chili, and radish, from 2014 to 2024 reveals that the proposed model significantly outperforms traditional methods, single deep learning models, and other hybrid models in predictive performance. Experimental results indicate substantial improvements in key metrics including the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R²), with R² values consistently exceeding 99.4% and achieving over 5% enhancement compared to the baseline GRU model. This research establishes a novel methodological framework for analyzing agricultural price forecasting while providing reliable technical support for market monitoring and policy regulation. Full article