Search Results (95)

Eggplant (Solanum melongena) is a significant vegetable in the Solanaceae family. Significant progress has been made in genetic diversity analysis and fingerprinting construction for crops such as tomatoes and peppers within the same family, but research on eggplants in these aspects remains relatively limited. Current germplasm identification using fingerprinting primarily relies on traditional SSR markers, which suffer from limited polymorphism and labor-intensive workflows. This study aimed to identify high-quality single nucleotide polymorphisms (SNPs), develop reliable Kompetitive Allele-Specific PCR (KASP) markers for eggplant genotyping, and then conduct fingerprint construction and genetic diversity analysis. The ultimate goals were to achieve a precise identification of eggplant varieties and deeply explore the genetic background and evolutionary patterns of eggplant germplasm. In this study, 49 representative eggplant accessions were re-sequenced. After data quality control, sequence alignment, and multiple rounds of screening, 224 high-quality SNPs were identified. Based on these SNPs, 96 SNPs were selected to develop KASP markers. These markers can provide abundant genetic markers for eggplant genetic research, which are used to deeply explore the genetic background and conduct genetic diversity analysis. After multiple rounds of rigorous verification, 32 core candidate markers were finally screened out. The average polymorphic information content (PIC) and gene diversity (GD) values were 0.36 and 0.46, respectively. Phylogenetic tree, population structure, and principal component analyses divided the 280 eggplant accessions into eight distinct groups. Through the analysis of minimal core markers and core germplasm, 23 core SNP markers and a subset of 56 core germplasm accessions were identified, leading to the establishment of a comprehensive fingerprinting system for all 280 accessions. Our findings provide a foundational genetic resource for eggplant germplasm identification and offer significant support for future breeding efforts. Full article

Plant fungal diseases remain a major threat to global agricultural production, necessitating eco-friendly and sustainable strategies. Conventional chemical fungicides often lead to the development of resistant pathogen strains and cause environmental contamination. Therefore, the development of biocontrol agents is particularly important. In this study, we identified Burkholderia stagnalis YJ-2 from the rhizosphere soil of Woodsia ilvensis as a promising biocontrol strain using 16S rRNA and whole-genome sequencing. This strain demonstrated broad-spectrum antifungal activity against plant fungal pathogens, with its bioactive extracts maintaining high stability across a temperature range of 25–100 °C and pH range of 2–12. We used in vitro assays to further show that the metabolites of B. stagnalis YJ-2 disrupted the hyphal morphology of Valsa mali, resulting in swelling, reduced branching, and increased pigmentation. Fluorescence labeling confirmed that B. stagnalis YJ-2 stably colonized the roots and stems of tomato and wheat plants. Furthermore, various formulations of microbial agents based on B. stagnalis YJ-2 were evaluated for their efficacy against plant pathogens. The seed-coating formulation notably protected tomato seedlings from Alternaria solani infection without affecting germination (p > 0.1), while the wettable powder exhibited significant control effects on early blight in tomatoes, with the preventive treatment showing better efficacy than the therapeutic treatment. Additionally, the B. stagnalis YJ-2 bone glue agent showed a substantial inhibitory effect on apple tree canker. Whole-genome analysis of B. stagnalis YJ-2 revealed a 7,705,355 bp genome (67.68% GC content) with 6858 coding genes and 20 secondary metabolite clusters, including three clusters (YJ-2_GM002015-YJ-2_GM002048, YJ-2_GM0020090-YJ-2_GM002133, and YJ-2_GM06534-YJ-2_GM006569) that are related to the antifungal activity of YJ-2 and are homologous to the biosynthetic gene clusters of known secondary metabolites, such as icosalide, ornibactin, and sinapigladioside. We further knocked out core biosynthetic genes of two secondary metabolic gene clusters and found that only the YJ-2_GM006534-YJ-2_GM006569 gene cluster had a corresponding function in two potential antifungal gene clusters. In contrast to the wild-type strain YJ-2, only deletion of the YJ-2_GM006563 gene reduced the antifungal activity of B. stagnalis YJ-2 by 8.79%. These findings highlight the biocontrol potential of B. stagnalis YJ-2, supporting a theoretical foundation for its development as a biocontrol agent against plant fungal diseases and thereby promoting sustainable agricultural disease management. Full article

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R² = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones. Full article

The U-box E3 ubiquitin ligase (PUB) gene family plays an important role in regulating plant responses to abiotic stress. Poncirus trifoliata (trifoliate orange), a citrus rootstock with notable cold, drought, and salt tolerance, serves as an excellent model for studying stress-responsive genes. In this study, a total of 47 PUB genes (PtrPUBs) were identified in the trifoliate orange genome. Chromosomal distribution analysis indicated that PtrPUB genes were unevenly distributed across nine trifoliate orange chromosomes. Phylogenetic tree analysis indicated that 170 PUB proteins from trifoliate orange, Arabidopsis thaliana, and tomato were clustered into five subfamilies. Gene structure, conserved domain, and motif analyses revealed diverse exon–intron and motif organizations of PtrPUB genes, suggesting potential functional differentiation among PtrPUBs. Cis-acting analysis indicated that the promoters of PtrPUB genes harbor elements related to hormone signaling (ABA, MeJA), drought stress, and low-temperature responses. Transcriptomic data and qRT-PCR results suggested that PtrPUB genes are responsive to ABA and dehydration treatments. This study provides a foundation for understanding the functional roles of PUB genes in trifoliate orange and offers insights for improving stress tolerance in citrus breeding programs. Full article

Tomato chlorosis virus (ToCV), first reported in Florida, USA, in 1998, has since emerged in multiple regions worldwide, posing a significant threat to global tomato production. However, its origin, migration patterns, and evolutionary history remain poorly understood. In this study, we used Bayesian phylogeographic analysis of coat protein gene sequences from 155 ToCV isolates to reconstruct its phylogeographic history. Our results show that ToCV evolves at a rate of 6.24 × 10⁻⁴ subs/site/year (95% credibility interval: 4.35 × 10⁻⁴–8.28 × 10⁻⁴), with the most recent common ancestor dating back to 1882. The maximum clade credibility (MCC) tree revealed three major clades, with Clade 1—whose most recent common ancestor dates to approximately 1975—comprising over 90% of the isolates. Although the exact origin of ToCV remains uncertain, we identified five distinct migration pathways: one from Europe to the Americas, one from Europe to South Asia, one from the Middle East to East Asia, one from East Asia to mainland China, and one from mainland China to Europe. These findings underscore the complex global spread of ToCV and suggest that multiple geographic areas have contributed to its ongoing evolution and dissemination. Full article

Endophytic Pseudomonas species from agricultural crops have been extensively studied for their plant-growth-promoting (PGP) potential, but little is known about their PGP potential when isolated from perennial trees. This study investigated the plant-growth-promoting (PGP) potential of an endophyte, Pseudomonas oryzihabitans GDW1, isolated from a healthy pine tree by taking tomato as a host plant. We employed multiomics approaches (transcriptome and bacteriome analyses) to elucidate the underlying PGP mechanisms of GDW1. The results of greenhouse experiments revealed that the application of GDW1 significantly improved tomato plant growth, increasing shoot length, root length, fresh weight, and biomass accumulation by up to 44%, 38%, 54%, and 59%, respectively, compared with control. Transcriptomic analysis revealed 1158 differentially expressed genes significantly enriched in the plant hormone signaling (auxin, gibberellin, and cytokinin) and stress response (plant–pathogen interaction, MAPK signaling pathway-plant, and phenylpropanoid biosynthesis) pathways. Protein–protein interaction network analysis revealed nine hub genes (MAPK10, ARF19-1, SlCKX1, GA2ox2, PAL5, SlWRKY37, GH3.6, XTH3, and NML1) related to stress tolerance, hormone control, and plant defense. Analysis of the tomato root bacteriome through 16S rRNA gene amplicon sequencing revealed that GDW1 inoculation dramatically altered the root bacterial community structure, enhancing the diversity and abundance of beneficial taxa (Proteobacteria and Bacteroidota). Co-occurrence network analysis showed a complex bacterial network in treated plants, suggesting increasingly intricate microbial relationships and improved nutrient absorption. Additionally, FAPROTAX and PICRUSt2 functional prediction analyses suggested the role of GDW1 in nitrogen cycling, organic matter degradation, plant growth promotion, and stress resistance. In conclusion, this study provides novel insights into the symbiotic relationship between P. oryzihabitans GDW1 and tomato plants, highlighting its potential as a biofertilizer for sustainable agriculture and a means of reducing the reliance on agrochemicals. Full article

Background: GA 2-oxidases (GA2oxs), a class of enzymes, inhibit the biosynthesis of bioactive gibberellins (GAs) in plants. The GA2 oxidase gene is crucial for regulating the passivation process of active GA and is widely involved in hormone signaling and abiotic stress processes. Objective/Methods: To examine the potential effects of the GA2 oxidase gene on Solanum pennellii, one of the important stress-tolerance wild species of tomato, a systematic analysis was performed to study the structure, phylogenetic tree, genomic locus, and upstream cis-regulatory elements of SpGA2ox genes. The expression patterns of the SpGA2ox family in various tissues were analyzed on the basis of published RNA-seq data, and the changes in SpGA2ox expression in the leaves of seedlings were detected under salinity stress and GA treatment by real-time fluorescence quantitative PCR. Results: We identified nine SpGA2ox genes in S. pennellii. They were located on chromosomes 1, 2, 4, 7, 8, and 10. The SpGA2ox family was clearly divided into three groups through phylogenetic relationship analysis, namely, five in C19-GA2ox class I, one in C19-GA2ox class II, and three in C20-GA2ox class. And cis-element analysis provided the basis for understanding the function of growth, development, hormones, and abiotic stress of GA2ox genes in S. pennellii. The expression patterns of the SpGA2ox family were different in three classes, and SpGA2ox1 exhibited higher expression levels in the stem compared to other tissues. The expression levels of all SpGA2ox genes increased significantly under salt stress and decreased by treatment with GA₃. With the largest changes in relative expression levels, SpGA2ox3 and SpGA2ox8 might exert key effects on the regulation of GA synthesis and the response to salt stress. Conclusions: The present study may be instrumental for further investigation into the impact of SpGA2oxs on responses to abiotic stress and provide potential targets for the genetic improvement of S. pennellii. Full article

Soil salinity, affecting 20–50% of irrigated farmland globally, poses a significant threat to agriculture and food security, worsened by climate change and increasing droughts. Traditional methods for managing saline soils—such as leaching, gypsum addition, and soil excavation—are costly and often unsustainable. An alternative approach using plant growth-promoting microorganisms (PGPMs) offers promise for improving crop productivity in saline conditions. This study tested twenty-three bacterial strains, one yeast, and one fungal strain, isolated from diverse sources including salicornia plants, sandy soils, tomato stems or seeds, tree leaves, stems, and flowers. They were initially submitted to in vitro selection tests to assess their ability to promote plant growth under salt stress. In vitro tests included auxin production, phosphate solubilization, and co-culture of microorganisms and tomato seedlings in salt-supplemented media. The Bacillus sp. strain 44 showed the highest auxin production, while Bacillus megaterium MJ had the strongest phosphate solubilization ability. Cryptococcus sp. STSD 4 and Gliomastix murorum (4)10-1(iso1) promoted germination and the growth of tomato seedlings in an in vitro co-culture test performed on a salt-enriched medium. This innovative test proved particularly effective in selecting relevant strains for in planta trials. The microorganisms that performed best in the various in vitro tests were then evaluated in vivo on tomato plants grown in greenhouses. The results showed significant improvements in growth, including increases in fresh and dry biomass and stem size. Among the strains tested, Gliomastix murorum (4)10-1(iso1) stood out, delivering an increase in fresh biomass of 94% in comparison to the negative control of the salt modality. These findings highlight the potential of specific PGPM strains to enhance crop resilience and productivity in saline soils, supporting sustainable agricultural practices. Full article

In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. In this study, fluorescence spectrum acquisition and hyperspectral imaging processing of tomato leaf samples with and without pesticides were conducted, and spectral data from regions of interest of hyperspectral images were extracted. The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. According to the results, R² of the model of hyperspectral data after SG-SNV preprocessing and PLSR modeling reached 0.9974, RMSEC reached 0.0221, and RMSEP reached 0.0565. R² of the model of fluorescence spectral data after SG-MSC preprocessing and SVM modeling reached 0.9986, RMSEC reached 0.2496, and RMSEP reached 0.4193. Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. Therefore, the fluorescence spectral and hyperspectral imaging techniques applied to tomato leaf pesticide detection enjoy a promising application prospect. Full article

This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model’s superior performance, achieving over 99% accuracy and significantly improving F1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification. Full article

Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (Solanum lycopersicum), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and financial losses to farmers. Accurate detection and classification of tomato pests are the primary steps of integrated pest management practices, which are crucial for sustainable agriculture. This paper explores using Convolutional Neural Networks (CNNs) to classify tomato pest images automatically. Specifically, we investigate the impact of various optimizers on classification performance, including AdaDelta, AdaGrad, Adam, RMSprop, Stochastic Gradient Descent (SGD), and Nadam. A diverse dataset comprising 4263 images of eight common tomato pests was used to train and evaluate a customized CNN model. Extensive experiments were conducted to compare the performance of different optimizers in terms of classification accuracy, convergence speed, and robustness. RMSprop achieved the highest validation accuracy of 89.09%, a precision of 88%, recall of 85%, and F1 score of 86% among the optimizers, outperforming other optimizer-based CNN architectures. Additionally, conventional machine learning models such as logistic regression, random forest, naive Bayes classifier, support vector machine, decision tree classifier, and K-nearest neighbors (KNN) were applied to the tomato pest dataset. The best optimizer-based CNN architecture results were compared with these machine learning models. Furthermore, we evaluated the cross-validation results of various optimizers for tomato pest classification. The cross-validation results demonstrate that the Nadam optimizer with CNN outperformed the other optimizer-based approaches and achieved a mean accuracy of 79.12% and F1 score of 78.92%, which is 14.48% higher than the RMSprop optimizer-based approach. The state-of-the-art deep learning models such as LeNet, AlexNet, Xception, Inception, ResNet, and MobileNet were compared with the CNN-optimized approaches and validated the significance of our RMSprop and Nadam-optimized CNN approaches. Our findings provide insights into the effectiveness of each optimizer for tomato pest classification tasks, offering valuable guidance for practitioners and researchers in agricultural image analysis. This research contributes to advancing automated pest detection systems, ultimately aiding in early pest identification and proactive pest management strategies in tomato cultivation. Full article

Background/Objectives: The sucrose non-fermentation-related kinase 1 (SnRK1) protein complex in plants plays an important role in energy metabolism, anabolism, growth, and stress resistance. SnRK1 is a heterotrimeric complex. The SnRK1 complex is mainly composed of α, β, βγ, and γ subunits. Studies on plant SnRK1 have primarily focused on the functional α subunit, with the β regulatory subunit remaining relatively unexplored. The present study aimed to elucidate the evolutionary relationship, structural prediction, and interaction with the core α subunit of peach SnRK1β3 (PpSnRK1) subunit. Methods: Bioinformatics analysis of PpSnRK1 was performed through software and website. We produced transgenic tomato plants overexpressing PpSnRK1 (OEPpSnRK1). Transcriptome analysis was performed on OEPpSnRK1 tomatoes. We mainly tested the growth index and drought resistance of transgenic tomato plants. Results: The results showed that PpSnRK1 has a 354 bp encoded protein sequence (cds), which is mainly located in the nucleus and cell membrane. Phylogenetic tree analysis showed that PpSnRK1β3 has similar domains to other woody plants. Transcriptome analysis of OEPpSnRK1β3 showed that PpSnRK1β3 is widely involved in biosynthetic and metabolic processes. Functional analyses of these transgenic plants revealed prolonged growth periods, enhanced growth potential, improved photosynthetic activity, and superior drought stress tolerance. Conclusions: The study findings provide insight into the function of the PpSnRK1 subunit and its potential role in regulating plant growth and drought responses. This comprehensive analysis of PpSnRK1 will contribute to further enhancing our understanding of the plant SnRK1 protein complex. Full article

In complex scenes, factors such as tree branches and leaves occlusion, dense distribution of tomato fruits, and similarity of fruit color to the background color make it difficult to correctly identify the ripeness of the tomato fruits when harvesting them. Therefore, in this study, an improved YOLOv8 algorithm is proposed to address the problem of tomato fruit ripeness detection in complex scenarios, which is difficult to carry out accurately. The algorithm employs several technical means to improve detection accuracy and efficiency. First, Swin Transformer is used to replace the third C2f in the backbone part. The modeling of global and local information is realized through the self-attention mechanism, which improves the generalization ability and feature extraction ability of the model, thereby bringing higher detection accuracy. Secondly, the C2f convolution in the neck section is replaced with Distribution Shifting Convolution, so that the model can better process spatial information and further improve the object detection accuracy. In addition, by replacing the original CIOU loss function with the Focal–EIOU loss function, the problem of sample imbalance is solved and the detection performance of the model in complex scenarios is improved. After improvement, the mAP of the model increased by 2.3%, and the Recall increased by 6.8% on the basis of YOLOv8s, and the final mAP and Recall reached 86.9% and 82.0%, respectively. The detection speed of the improved model reaches 190.34 FPS, which meets the demand of real-time detection. The results show that the improved YOLOv8 algorithm proposed in this study exhibits excellent performance in the task of tomato ripeness detection in complex scenarios, providing important experience and guidance for tomato ripeness detection. Full article

Tobacco curly shoot virus (TbCSV), a begomovirus, causes significant economic losses in tobacco and tomato crops across East, Southeast, and South Asia. Despite its agricultural importance, the evolutionary dynamics and emergence process of TbCSV remain poorly understood. This study analyzed the phylodynamics of TbCSV by examining its nucleotide sequences of the coat protein (CP) gene collected between 2000 and 2022. Using various combinations of priors, Bayes factor comparisons identified heterochronous datasets (3 × 100 million chains) generated from a strict molecular clock and Bayesian skyline tree priors as the most robust. The mean substitution rate of the CP gene was estimated at 6.50 × 10⁻⁴ substitutions/site/year (95% credibility interval: 4.74 × 10⁻⁴–8.50 × 10⁻⁴). TbCSV was inferred to have diverged around 1920 CE (95% credibility interval: 1887–1952), with its most probable origin in South Asia. These findings provide valuable insights for the phylogeography and evolutionary dynamics of TbCSV, and contribute to a broader understanding of begomovirus epidemiology. Full article

Saffron corm rot (SCR), the most serious disease affecting saffron, has been confirmed to be caused by Fusarium oxysporum in previous studies. Compared to other fungal species, F. oxysporum exhibits host specialization, a special phenomenon associated with the secreted in xylem (SIX) genes. This study examined the pathogenicity specialization of F. oxysporum isolated from saffron corms with SCR disease. The results showed that this F. oxysporum strain was strongly pathogenic to saffron corms, causing SCR; weakly pathogenic to the corms of freesia, which is in the Iridaceae family along with saffron; and not pathogenic to watermelon, melon, and tomato. Other formae speciales of F. oxysporum were not pathogenic to saffron corms. This suggests that F. oxysporum saffron strains exhibit obvious pathogenicity specialization for Iridaceae spp. Subsequently, the F. oxysporum saffron strain (XHH35) genome was sequenced, and a comparative genomics study of XHH35 and three other formae speciales was conducted using OrthoVenn3. XHH35 contained 90 specific genes absent in the other three formae speciales. These genes are involved in certain key biological processes and molecular functions. Based on BLAST homology searching, the F. oxysporum saffron strain (XHH35) genome was predicted to contain seven SIX genes (SIX 4, SIX 6, SIX 7, SIX 10, SIX 11, SIX 12, and SIX 14) highly homologous to F. oxysporum f. sp. lycopersici, which was verified using polymerase chain reaction (PCR) amplification. The corresponding individual phylogenetic tree indicated that the F. oxysporum saffron strain (XHH35) showed a separate branch with different formae speciales. This study is the first-ever report of F. oxysporum f. sp. crocus, a new forma specialis. Based on the specificity of its SIX genes, the SIX 10 gene was selected to further establish a rapid identification technique for F. oxysporum f. sp. crocus, which will be useful in future research. Full article