Search Results (877)

In this study, GH19 chitinase (Chi) gene family was systematically identified and characterized using genomic assemblies from four cotton species: Gossypium barbadense, G. hirsutum, G. arboreum, and G. raimondii. A suite of analyses was performed, including genome-wide gene identification, physicochemical property characterization of the encoded proteins, subcellular localization prediction, phylogenetic reconstruction, chromosomal mapping, promoter cis-element analysis, and comprehensive expression profiling using transcriptomic data and qRT-PCR (including tissue-specific expression, hormone treatments, and Fusarium oxysporum infection assays). A total of 107 GH19 genes were identified across the four species (35 in G. barbadense, 37 in G. hirsutum, 19 in G. arboreum, and 16 in G. raimondii). The molecular weights of GH19 proteins ranged from 9.9 to 97.3 kDa, and they were predominantly predicted to localize to the extracellular space. Phylogenetic analysis revealed three well-conserved clades within this family. In tetraploid cotton, GH19 genes were unevenly distributed across 12 chromosomes, often clustering in certain regions, whereas in diploid species, they were confined to five chromosomes. Promoter analysis indicated that GH19 gene promoters contain numerous stress- and hormone-responsive motifs, including those for abscisic acid (ABA), ethylene (ET), and gibberellin (GA), as well as abundant light-responsive elements. The expression patterns of GH19 genes were largely tissue-specific; for instance, GbChi23 was predominantly expressed in the calyx, whereas GbChi19/21/22 were primarily expressed in the roots and stems. Overall, this study provides the first comprehensive genomic and functional characterization of the GH19 family in G. barbadense, laying a foundation for understanding its role in disease resistance mechanisms and aiding in the identification of candidate genes to enhance plant defense against biotic stress. Full article

Atriplex hortensis var. rubra (red orache, RO) is a halotolerant species rich in nutraceutical compounds, which makes it a valuable crop for human nutrition. This plant could also be exploited for phytoremediation of contaminated soil and wastewater, and for saline aquaponics. A root rot disease was observed on hydroponically grown RO plants, caused by Pythium deliense and Pythium Cluster B2a sp. Identification was based on morphology, molecular analysis (ITS and COI), and phylogenetic analysis. We assessed disease severity in plants grown in a growth chamber with nutrient solutions containing different NaCl concentrations (0, 7, and 14 g L⁻¹ NaCl). In vitro growth at different salinity levels and temperatures was also evaluated. Both Pythium species were pathogenic but showed different responses. Pythium deliense was significantly more virulent than Pythium Cluster B2a sp., causing a steady reduction in root dry weight (RDW) of 70% across all salinity levels. Pythium Cluster B2a sp. reduced RDW by 50% at 0 and 7 g L⁻¹ NaCl while no symptoms were observed at 14 g L⁻¹ NaCl. Pythium deliense grew best at 7 and 14 g L⁻¹ NaCl, while Pythium Cluster B2a sp. growth was reduced at 14 g L⁻¹ NaCl. Both pathogens had an optimum temperature of 30 °C. This is the first report of Pythium spp. causing root rot on RO grown hydroponically. The effective use of halophytic crops must consider pathogen occurrence and fitness in saline conditions. Full article

In recent years, research on plant disease detection has combined artificial intelligence, hyperspectral imaging, unmanned aerial vehicle remote sensing, and other technologies, promoting the transformation of pest and disease control in smart agriculture towards digitalization and artificial intelligence. This review systematically elaborates on the research status of non-destructive detection techniques used for plant disease identification and detection, mainly introducing the following two types of methods: spectral technology and imaging technology. It also elaborates, in detail, on the principles and application examples of each technology and summarizes the advantages and disadvantages of these technologies. This review clearly indicates that non-destructive detection techniques can achieve plant disease and pest detection quickly, accurately, and without damage. In the future, integrating multiple non-destructive detection technologies, developing portable detection devices, and combining more efficient data processing methods will become the core development directions of this field. Full article

Potatoes are one of the world’s most widely cultivated crops, but their yield is coming under mounting pressure from early blight, a fungal disease caused by Alternaria solani. Early detection and accurate identification are key to effective disease management and yield protection. This paper introduces a novel deep learning framework called ZeroShot CNN, which integrates convolutional neural networks (CNNs) and ZeroShot Learning (ZSL) for the efficient classification of seen and unseen disease classes. The model utilizes convolutional layers for feature extraction and employs semantic embedding techniques to identify previously untrained classes. Implemented on the Kaggle potato disease dataset, ZeroShot CNN achieved 98.50% accuracy for seen categories and 99.91% accuracy for unseen categories, outperforming conventional methods. The hybrid approach demonstrated superior generalization, providing a scalable, real-time solution for detecting agricultural diseases. The success of this solution validates the potential in harnessing deep learning and ZeroShot inference to transform plant pathology and crop protection practices. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

SMoV and SVBV are two major viruses that pose significant threats to the global strawberry industry. Both are latent viruses, making early detection difficult due to their uneven distribution and low concentration in host tissues. Traditional RT-PCR techniques are insufficient for precise and quantitative detection. In this study, TaqMan RT-qPCR detection systems for SMoV and SVBV were established for application in practical production settings, enabling accurate, rapid, and efficient detection of strawberry viruses. When viral accumulation in plants is low, the highly sensitive TaqMan RT-qPCR technique allows for accurate quantification, facilitating the early identification of infected plants and preventing large-scale outbreaks in cultivation areas. The development of a duplex TaqMan RT-qPCR assay enables simultaneous quantification of SMoV and SVBV in a single reaction, improving detection efficiency and providing technical support for risk assessment and effective control of strawberry viral diseases. 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

WRKY transcription factors play key roles in plant immune responses, including resistance to fungal pathogens. In the present study, we identified a flax resistance-related gene Lus10021999, named LuWRKY39. Here, to identify the role of WRKY transcription factor in resistance of flax against Septoria linicola, we cloned and analyzed the gene LuWRKY39 via homologous cloning using bioinformatics methods and localized the encoded protein. Quantitative real-time PCR (qRT-PCR) was used to explore the response of this gene to S. linicola. The results showed that the gene that is 948 bp long exhibited the closest genetic relationship to WRKY in castor (Ricinus communis), as revealed by phylogenetic analysis, and the encoded protein was localized in the nucleus. The LuWRKY39 gene showed higher expression levels in resistant flax materials than in susceptible ones, and higher in roots and stems than in leaves. Furthermore, gene expression showed an upward trend following treatment with salicylic acid (SA) and methyl jasmonate (MeJA), indicating that LuWRKY39 is involved in the regulation of SA and JA signals. By silencing LuWRKY39 in flax using virus-induced gene silencing (VIGS), the processed plants were more sensitive to S. linicola than untreated plants. Gene expression analysis and disease index statistics confirmed that the silenced plants were more susceptible, highlighting the crucial role of LuWRKY39 in flax disease resistance. This study provides a foundation for functional investigations of WRKY genes in flax and the identification of disease resistance genes. Full article

Timely and accurate identification of plant diseases is critical to mitigating crop losses and enhancing yield in precision agriculture. This paper proposes AgriFusionNet, a lightweight and efficient deep learning model designed to diagnose plant diseases using multimodal data sources. The framework integrates RGB and multispectral drone imagery with IoT-based environmental sensor data (e.g., temperature, humidity, soil moisture), recorded over six months across multiple agricultural zones. Built on the EfficientNetV2-B4 backbone, AgriFusionNet incorporates Fused-MBConv blocks and Swish activation to improve gradient flow, capture fine-grained disease patterns, and reduce inference latency. The model was evaluated using a comprehensive dataset composed of real-world and benchmarked samples, showing superior performance with 94.3% classification accuracy, 28.5 ms inference time, and a 30% reduction in model parameters compared to state-of-the-art models such as Vision Transformers and InceptionV4. Extensive comparisons with both traditional machine learning and advanced deep learning methods underscore its robustness, generalization, and suitability for deployment on edge devices. Ablation studies and confusion matrix analyses further confirm its diagnostic precision, even in visually ambiguous cases. The proposed framework offers a scalable, practical solution for real-time crop health monitoring, contributing toward smart and sustainable agricultural ecosystems. Full article

Root rot, a globally devastating disease of common bean (Phaseolus vulgaris L.), remains a major constraint on bean production across China. Despite its agricultural impact, the pathogen complex associated with this disease has been poorly characterized in most provinces. To address this critical knowledge gap, we conducted nationwide surveys during 2016–2018, systematically sampling 1–10 symptomatic plants from each of 121 (2016) and 170 (2018) field sites across 17 provinces in China’s major vegetable production regions. Isolates obtained from symptomatic root tissues underwent morphological screening, followed by molecular identification using partial sequences of EF1-α for Fusarium species and ITS regions for other genera. Pathogenicity of representative isolates was subsequently confirmed through controlled greenhouse assays. This integrated approach revealed fourteen fungal and oomycete genera, with Fusarium (predominantly F. oxysporum and F. solani) and Rhizoctonia (R. solani) emerging as the most prevalent pathogens. Notably, pathogen composition exhibited significant regional variation and underwent temporal shifts across developmental stages. Additionally, F. oxysporum, F. solani, and R. solani demonstrated significant interspecies associations with frequent co-occurrence in bean root rot systems. Collectively, this first comprehensive characterization of China’s common bean root rot complex not only clarifies spatial–temporal pathogen dynamics but also provides actionable insights for developing region- and growth stage-specific management strategies, particularly through targeted control of dominant pathogens during key infection windows. Full article

Crop diseases pose a significant threat to agricultural productivity and global food security. Timely and accurate disease identification is crucial for improving crop yield and quality. While most existing deep learning-based methods focus primarily on image datasets for disease recognition, they often overlook the complementary role of textual features in enhancing visual understanding. To address this problem, we proposed a cross-modal data fusion via a vision-language model for crop disease recognition. Our approach leverages the Zhipu.ai multi-model to generate comprehensive textual descriptions of crop leaf diseases, including global description, local lesion description, and color-texture description. These descriptions are encoded into feature vectors, while an image encoder extracts image features. A cross-attention mechanism then iteratively fuses multimodal features across multiple layers, and a classification prediction module generates classification probabilities. Extensive experiments on the Soybean Disease, AI Challenge 2018, and PlantVillage datasets demonstrate that our method outperforms state-of-the-art image-only approaches with higher accuracy and fewer parameters. Specifically, with only 1.14M model parameters, our model achieves a 98.74%, 87.64% and 99.08% recognition accuracy on the three datasets, respectively. The results highlight the effectiveness of cross-modal learning in leveraging both visual and textual cues for precise and efficient disease recognition, offering a scalable solution for crop disease recognition. Full article

Poplar leaves (Populi folium) are a herbal remedy traditionally used for the treatment of rheumatic diseases and prostate inflammation. The aim of our study was a comprehensive identification of secondary metabolites occurring in the leaves of Populus alba, Populus × candicans, and Populus nigra, in order to search for a source of raw plant material rich in active compounds. Total salicylate (TSC), flavonoid (TFC), and phenolic compound (TPC) contents were determined, and the antioxidant potential was assessed using DPPH (2,2-diphenyl-1-picrylhydrazyl), ABTS (2,2′-azino-bis(3-ethylbenzothiazoline- 6-sulfonic acid) diammonium salt), and FRAP (ferric reducing antioxidant power) assays as well as 2D-TLC (two-dimensional thin layer chromatography) bioautography using DPPH, riboflavin-light-NBT (nitro blue tetrazolium chloride), and xanthine oxidase inhibition tests. Secondary metabolites present in the analyzed poplar leaves were identified under the developed HPLC-DAD-ESI/MS (high performance liquid chromatography with photodiode array detection and electrospray ionization mass spectrometric detection analysis conditions and using the 2D-TLC method. Among the 80 identified compounds, 13 were shown for the first time in the genus Populus. The most diverse and similar set of flavonoids characterized the leaves of P. × candicans and P. nigra, while numerous salicylic compounds were present in the leaves of P. alba and P. × candicans. All analyzed leaves are a rich source of phenolic compounds. The highest flavonoid content was found in the leaves of P. × candicans and P. nigra, while the leaves of P. alba were characterized by the highest content of salicylates. All examined poplar leaves demonstrated antioxidant potential in all the assays used, which decreased in the following order: P. nigra, P. × candicans, P. alba. Full article

The purpose of this study was to investigate the growth-promoting effects of four rhizobacterial isolates (RS60, RS65, RS46, and RP6) isolated from the tomato rhizosphere. These isolates were screened for key plant growth-promoting rhizobacteria (PGPR) mechanisms, including ammonia production, nitrogen fixation, phosphate solubilization, indole-3-acetic acid (IAA) production, and siderophore synthesis. Their potential to enhance seed germination and tomato plant growth was investigated in controlled and greenhouse conditions. Four isolates exhibited multiple PGPR attributes, notably IAA and ammonia production as well as phosphate solubilization. The results revealed that these strains significantly enhanced tomato seed germination and shoot growth in vitro, with RS65 showing the highest germination rate (70%). However, no significant differences in early seedling responses were observed under greenhouse conditions when compared to the control. Thirty days after inoculation, greenhouse results revealed that the four studied strains significantly increased growth metrics including shoot length, number of leaves, collar diameter, and dry weight. The isolate RP6 showed a significant effect on the growth of the plant, with an average shoot length of 34.40 cm and nine leaves per plant. In vitro antagonism assays demonstrated that isolates RS60, RS65, and RP6 effectively inhibited the growth of Botrytis cinerea, Alternaria alternata, and Oidium lycopersici, with inhibition rates exceeding 65%. These antagonistic activities were linked to the production of hydrolytic enzymes (chitinase, cellulase, pectinase, protease), siderophores, and hydrogen cyanide (HCN). Molecular identification through 16S rRNA gene sequencing confirmed the isolates as Bacillus cereus (RS60), Bacillus pumilus (RS46), Bacillus amyloliquefaciens (RP6), and Bacillus velezensis (RS65), each showing over 97% sequence similarity with reference strains. These findings underscore the potential of the selected Bacillus spp. as promising biofertilizers and biocontrol agents for sustainable tomato cultivation and support their inclusion in integrated disease and nutrient management strategies. Full article

Fusariosis is an infection caused by the fungus Fusarium spp., which is pathogenic to both plants and humans. The disease presents several clinical manifestations and epidemiological patterns. Current treatment relies on azoles and polyenes, but increasing antifungal resistance requires the exploration of new therapeutic options. This study reviewed patents related to the treatment of Fusariosis from the last 15 years (up to June 2023). The search identified 318 patents, categorized by identification code, publication date, type of application and mechanism of action, using the International Patent Classification and Cooperative Patent Classification systems. In addition, we conducted a bibliographic search in the PubMed database using the same criteria to identify the number of scientific articles. Of the 318 patents, 21 targeted Fusarium infections in humans. The years 2014 and 2018 stood out with three patents each, while the same period recorded an average of 58 published articles. The patents addressed mechanisms such as drug delivery, gene expression, immunotherapy, engineered drugs, and novel compounds. This research highlights the urgent need for continued innovation in therapeutic technologies to effectively treat Fusarium wilt. Full article

Among grapevine trunk diseases, Eutypa dieback, caused by the fungus Eutypa lata, is one of the most critical ones, due to its widespread infection in vineyards and the lack of effective treatments. This fungus is a vascular pathogen that enters grapevines through pruning wounds. The infection process is associated with phytotoxic metabolites produced by the fungus, and as such, the identification of new metabolites from different culture conditions and broths could provide valuable insights into the fungus’s enzymatic system and help its control. For the purposes of this study, the OSMAC (one strain, many compounds) approach was applied to investigate the secondary metabolism of E. lata strain 311 isolated from Vitis vinifera plants in Spain. A total of twenty metabolites were isolated, including five reported for the first time from E. lata and four that are newly identified compounds in the literature: eulatagalactoside A, (R)-2-(4′-hydroxy-3′-methylbut-1′-yn-1′-yl)-4-(hydroxymethyl)phenol, (S)-7-hydroxymethyl-3-methyl-2,3-dihydro-1-benzoxepin-3-ol, and (3aR,4S,5R,7aS)-4,5-dihydroxy-6-((R)-3′-methylbuta-1′,3′-dien-1′-ylidene)hexahydrobenzo[d][1,3]dioxol-2-one. These compounds were extracted from fermentation broths using silica gel column chromatography and high-performance liquid chromatography (HPLC). Their structures were elucidated through extensive 1D and 2D NMR spectroscopy, along with high-resolution electrospray ionization mass spectrometry (HRESIMS). Compounds were evaluated for phytotoxicity against Phaseolus vulgaris, with only eulatagalactoside A producing white spots after 48 h. Additionally, the antibacterial activity against Escherichia coli, Staphylococcus aureus, and Klebsiella pneumoniae of selected compounds was tested. The compounds (R)-2-(4′-hydroxy-3′-methylbut-1′-yn-1′-yl)-4-(hydroxymethyl)phenol and (S)-7-(hydroxymethyl)-3-methyl-2,3-dihydrobenzo[b]oxepin-3-ol showed the most significant antimicrobial activity against Gram-positive bacteria, inhibiting S. aureus by over 75%, with IC₅₀ values of 511.4 µg/mL and 617.9 µg/mL, respectively. Full article