Plant Diseases Research Articles

Insight into the new infection pathway resulting from above-ground pathogen infection of grapevine crown gall.

Grapevine crown gall (GCG), a soil-borne plant disease caused by tumorigenic Allorhizobium vitis (TAV) (=tumorigenic Rhizobium vitis) strains, poses a significant threat to grapevines worldwide. Recently, outbreaks of GCG have been reported in several vineyards, necessitating investigation into potential alternative infection pathways beyond soil transmission. The spatiotemporal distribution of GCG in vineyards from 2020 to 2022 was analyzed using the binary power law (BPL) model, with variations in quadrat shapes. Both total and newly observed diseased plants exhibited an aggregated distribution, indicating that new infections clustered around existing diseased plants, with secondary infections appearing as independent cluster points. This study provides evidence that infected pruning tools can transmit the pathogen to healthy grapevines and that TAV inoculation through spraying contributes more to GCG incidence than planting in infected soil alone. This represents the first documented case of secondary above-ground TAV infection contributing to GCG in commercial vineyards.

Antibacterial Properties of Plant Extracts against the Pathogen of Rosaceae Plants Erwinia amylovora

Erwinia amylovora is a devastating pathogen of plants of the family Rosaceae causing blight on the leaves, shoots, and flowers. Orchard trees suffer the most, however, other plants, like roses, strawberries, raspberries, and hawthorn, can also be affected. Due to the extreme use of chemicals for plant protection, alternatives like plant extracts have been studied recently. Some of the research shows a good potential to control plant diseases, however, only a symbolic number of plant species have been tested, and also, on a restricted number of pathogens. The information on the activity of plant extracts against E. amylovora is scarce. In this study, we prepared plant extracts from fifteen plant species and tested them against the plant pathogenic bacteria E. amylovora. The products were prepared by Soxhlet extraction with methanol and the following concentration according to an established procedure. The finished products were diluted and tested for antibacterial activity in different concentrations in vitro against seven reference strains of E. am¬ylovora, isolated from different hosts, such as pear, apple, quince, aronia, hawthorn, strawberry, and musk strawberry. Extracts from the roots of Geranium macrorrhizum and fruits of Chaenomeles sp. genotypes showed promising potentialities for further experiments on the control of E. amylovora.

Enhancing plant disease detection: a novel CNN-based approach with tensor subspace learning and HOWSVD-MDA

Enhancing plant disease detection: a novel CNN-based approach with tensor subspace learning and HOWSVD-MDA

Revolutionizing food security: The transformative role of fungi in the 21st century

With increasing global population density and climate change, the sustainability of food production poses new challenges. These are factors such as food inflation, climate change, natural disasters, and diseases affecting crops among others. Consequently, fungi have evolved as a pillar of ecological agriculture in the twenty-first century. Some of the diverse applications of fungi include biological control, synthesis of antifungal compounds, symbiotic relationship with plants, and enhancement of soil structure. They also function as decomposers in ensuring nutrient availability in the soil, and as biostimulants to enhance plants’ growth, plant disease management and enhanced resistance to abiotic stressors such as water scarcity, and salinity and mitigate the impact of climate change. Furthermore, fungi play other important roles in food security, such as in the processing of various foods including cheese, bread, fermented products and other sources of protein, vitamins, and dietary fiber, while foods of fungi origin prevent lifestyle diseases. New approaches in fungal biotechnology present the hope of eliminating hunger and malnutrition through food production, preservation, and packaging among others. Although, challenges persist in the management of pathogenic fungi in agriculture, however, it is important to leverage the beneficial role of fungi and encourage further exploration of its transformative potential towards a secured global food security.

Resistance Mechanisms of Plant Pathogenic Fungi to Fungicide, Environmental Impacts of Fungicides, and Sustainable Solutions

The significant reduction in agricultural output and the decline in product quality are two of the most glaring negative impacts caused by plant pathogenic fungi (PPF). Furthermore, contaminated food or transit might introduce mycotoxins produced by PPF directly into the food chain. Eating food tainted with mycotoxin is extremely dangerous for both human and animal health. Using fungicides is the first choice to control PPF or their toxins in food. Fungicide resistance and its effects on the environment and public health are becoming more and more of a concern, despite the fact that chemical fungicides are used to limit PPF toxicity and control growth in crops. Fungicides induce target site alteration and efflux pump activation, and mutations in PPF result in resistance. As a result, global trends are shifting away from chemically manufactured pesticides and toward managing fungal plant diseases using various biocontrol techniques, tactics, and approaches. However, surveillance programs to monitor fungicide resistance and their environmental impact are much fewer compared to bacterial antibiotic resistance surveillance programs. In this review, we discuss the PPF that contributes to disease development in plants, the fungicides used against them, factors causing the spread of PPF and the emergence of new strains, the antifungal resistance mechanisms of PPF, health, the environmental impacts of fungicides, and the use of biocontrol agents (BCAs), antimicrobial peptides (AMPs), and nanotechnologies to control PPF as a safe and eco-friendly alternative to fungicides.

Understanding the Diversity, Evolution, Ecology, and Applications of Mycoviruses.

Mycoviruses are widely distributed among various kinds of fungi. Over the past 10 years, more novel mycoviruses have been discovered with the use of high-throughput sequencing techniques, and research on mycoviruses has made fantastic progress, promoting our understanding of the diversity, classification, evolution, and ecology of the entire virosphere. Mycoviruses affect the biological and ecological functions of their hosts, for example, by suppressing or improving hosts' virulence and reproduction ability, and subsequently affect the microbiological community where their hosts live; hence, we may develop mycoviruses to regulate the health of environments, plants, animals, and human beings. In this review, we introduce recently discovered mycoviruses from fungi of humans, animals, plants, and environments, and their diversity, evolution, and ecological characteristics. We also present the potential application of mycoviruses by describing the latest progress on using mycoviruses to control plant diseases. Finally, we discuss the main issues facing mycovirus research in the future.

Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes

Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes

RNAi-Based Approaches to Control Mycotoxin Producers: Challenges and Perspectives.

Mycotoxin contamination of food and feed is a worldwide problem that needs to be addressed with highly efficient and biologically safe techniques. RNA interference (RNAi) is a natural mechanism playing an important role in different processes in eukaryotes, including the regulation of gene expression, maintenance of genome stability, protection against viruses and others. Recently, RNAi-based techniques have been widely applied for the purposes of food safety and management of plant diseases, including those caused by mycotoxin-producing fungi. In this review, we summarize the current state-of-the-art RNAi-based approaches for reducing the aggressiveness of key toxigenic fungal pathogens and mycotoxin contamination of grain and its products. The ways of improving RNAi efficiency for plant protection and future perspectives of this technique, including progress in methods of double-stranded RNA production and its delivery to the target cells, are also discussed.

PENDAMPINGAN PEMBUATAN PUPUK DAN PESTISIDA ORGANIK GUNA MENDUKUNG USAHA BUDIDAYA STROBERI PADA KELOMPOK TANI SIDA URIP DESA SERANG KEC. KARANGREJA PURBALINGGA

Strawberries have various benefits for human health so they are widely loved by the public. Serang Village, Karangreja District, Purbalingga. This village is located on the slopes of Mount Slamet with an altitude of about 650 - 1,600 m dp is one of the centers of strawberry cultivation in Central Java, but since 2016 it has experienced a decline in production due to poor quality seeds, high fertilizer prices, declining soil fertility and the existence of pests and plant diseases. The purpose of this research-based community service activity is to implement the results of the research to solve the problems that exist in the Sida Urip farmer group, provide training in making organic fertilizers and pesticides, assist in the practice of making organic fertilizers and pesticides, improve skills in maintaining strawberry plants, so that it can increase strawberry production The method used in this activity is Rural Appraisal Participation, which is the active involvement of all elements of the target audience, the stages of the activity include: Survey, socialization of activities, training, practice, demonstration plot making and evaluation. The results of the activity showed that there was an increase in the knowledge and skills of farmer groups in making compost and biological agents to support environmentally friendly strawberry cultivation. The eco-friendly strawberry demonstration plot produces 38.69 kg/bed of strawberries and conventional strawberry cultivation land produces 37.45 kg/bed. The assistance carried out by the service team also increased enthusiasm and cohesiveness among members of the Sida Urip farmer group.

Machine Learning-based Prediction of African Swine Fever (ASF) in Pigs

African Swine Fever (ASF) is a contiguous viral disease of the pig with serious economic threats to the pork industry. Early identification of ASF infection is important to support sustainable developments in the ASF industry. There is also a need for a solution to identify the ASF infection as early as possible based on apparent symptoms of ASF to screen the infected animals, that are not targeted in the existing literature. Many machine learning (ML) solutions have been proposed in recent years for the prediction and identification of human, animal, and plant diseases. To deal with ASF in pigs ML-assisted model is proposed for the early identification of ASF infection without medical diagnosis and expert opinion. The data regarding apparent symptoms are collected from Chinese small pig farms. The loss of appetite, weakness, diarrhea, vomiting, coughing, skin redness, and breathing difficulty levels are taken as major apparent symptoms of ASF infection. Moreover, different ML models are also evaluated for their performance in the prediction of ASF infection based on selected apparent symptoms of ASF infection. In this regard, Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree (DT), Random Forests (RF), and Gaussian Naïve Bayes ML models are evaluated for ASF infection prediction. The implementation of the proposed solution reveals that the GNB model is more accurate as compared to the other evaluated models for the identification of ASF infection from the apparent ASF symptoms in infected pig animals, with 94.31\% accuracy. The proposed solution would be very effective in the early screening of ASF-infected pig animals without medical diagnosis and expert judgment.

The identification of a key gene highlights macrocyclic ring’s role in trichothecene toxicity

Trichothecenes are toxins produced by certain species from several fungal genera, including Aspergillus, Fusarium, Isaria, Paramyrothecium, Stachybotrys, Trichoderma, and Trichothecium. These toxins are of interest because they contribute to the toxigenicity, plant pathogenicity, and/or biological control activities of some fungi. All trichothecenes have the same core (12,13-epoxytrichothec-9-ene or EPT) structure but can differ from one another by the presence or absence of a macrocyclic ring formed from polyketide and isoprenoid substituents esterified to carbon atoms 4 and 15 of EPT, respectively. Genes required for formation and some modifications of EPT have been elucidated, but almost nothing is known about genes specific to the formation of the macrocyclic ring. Therefore, we used genomic, transcriptomic, metabolomic, and gene deletion analyses to identify genes that are required specifically for the formation of the macrocyclic ring. These analyses identified one gene, TRI24, that is predicted to encode an acyltransferase and that is required for macrocyclic ring formation during biosynthesis of macrocyclic trichothecenes by the fungus Paramyrothecium roridum. In addition, a TRI24 deletion mutant of P. roridum caused less severe disease symptoms on common bean and had less antifungal activity than its wild-type progenitor strain. We propose that the reduced aggressiveness and antifungal activity of the mutant resulted from its inability to produce trichothecenes with a macrocyclic ring. To our knowledge, this is the first report of a gene required specifically for the formation of the macrocyclic ring of trichothecenes and that loss of the macrocyclic ring of trichothecenes can alter the biological activities of a fungus.Key points• TRI24 gene is found in all known macrocyclic trichothecene-producing fungi.• A tri24-deletion mutant exhibits a reduction in antifungal and plant disease activities.• TRI24 is the first described gene specific to macrocyclic trichothecene biosynthesis.Graphical abstract

Regulatory mechanism of C4-dicarboxylates in cyclo (Phe-Pro) production

Cyclo (Phe-Pro) (cFP), a cyclic dipeptide with notable antifungal, antibacterial, and antiviral properties, shows great promise for biological control of plant diseases. Produced as a byproduct by non-ribosomal peptide synthetases (NRPS), the regulatory mechanism of cFP biosynthesis remains unclear. In a screening test of 997 Tn5 mutants of Burkholderia seminalis strain R456, we identified eight mutants with enhanced antagonistic effects against Fusarium graminearum (Fg). Among these, mutant 88’s culture filtrate contained cFP, confirmed through HPLC and LC-MS, which actively inhibited Fg. The gene disrupted in mutant 88 is part of the Dct transport system (Dct-A, -B, -D), responsible for C4-dicarboxylate transport. Knockout mutants of Dct genes exhibited higher cFP levels than the wild type, whereas complementary strains showed no significant difference. Additionally, the presence of exogenous C4-dicarboxylates reduced cFP production in wild type R456, indicating that these substrates negatively regulate cFP synthesis. Given that cFP synthesis is related to NRPS, we previously identified an NRPS cluster in R456, horizontally transferred from algae. Specifically, knocking out gene 2061 within this NRPS cluster significantly reduced cFP production. A Fur box binding site was predicted upstream of gene 2061, and yeast one-hybrid assays confirmed Fur protein binding, which increased with additional C4-dicarboxylates. Knockout of the Fur gene led to up-regulation of gene 2061 and increased cFP production, suggesting that C4-dicarboxylates suppress cFP synthesis by enhancing Fur-mediated repression of gene 2061.

Disease risk of the foliar endophyte Colletotrichum from invasive Ageratina adenophora to native plants and crops

The disease risk driven by foliar fungi asymptomatically infecting invasive plants has rarely been determined. In this study, we selected 44 foliar Colletotrichum endophytes isolated from the invasive plant Ageratina adenophora that are phylogenetically closely related to the C. gloeosporioides complex, C. boninense complex, C. orchidearum complex, and C. acutatum complex and evaluated their potential virulence and ability to transmit spores to native plants and cash crops both in vitro and in vivo. We verified that some foliar endophyte Colletotrichum strains adversely affect the leaf and seed germination of native plants and cash crops. Some strains reduce the growth and yield of tomato (SL) (Solanum lycopersicum) and pepper (CA) (Capsicum annuum) plants and even cause the death of strawberry (FA) plants (Fragaria ananassa). Moreover, we confirmed that A. adenophora leaves could horizontally transmit Colletotrichum to surrounding crop plants in vivo. Therefore, a high abundance of Colletotrichum asymptomatically associated with A. adenophora leaves might increase disease risk in surrounding native plants and cash crops. Our results provide a new perspective for regional ecological risk assessment of invasive plants and prevention of economic plant diseases in the invaded range.

Analysis of existing techniques for image-based recognition of agricultural crops diseases

An analysis of existing methods for processing and identifying the disease of agricultural crops was carried out. Other methods for identifying informative regions, including Fourier transformation, k-means clustering, Histogram Equalization, Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) algorithms, as well as their combinations were reviewed. The studied approaches demonstrated high accuracy in the identification and classification of various plant diseases. The study examined hybrid models, such as, for example, logistic regression with decision trees and Extreme Learning Machines (ELM). The accuracy of the Support Vector Machine (SVM), ELM, and Decision Trees algorithms were compared, convinced, that the importance of choosing the right parameters and fine-tuning to improve accuracy is important. The methods of deep learning such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and their usage in the scope of the image recognition, which are also used for disease recognition, were examined. The recognition accuracy of several CNN models was compared: DenseNet121, MobileNetV2, NASNetMobile and EfficientNetB0, and the last demonstrated the best results. The modification of ready-made architectures, including the architecture of the EfficientNetB0 neural network, has been analyzed as a way of adapting existing models to specific recognition requirements.

Noninvasive Raman spectroscopy for the detection of rice bacterial leaf blight and bacterial leaf streak

Plant diseases pose significant threats to agricultural yields and are responsible for nearly 20 % of losses in total food production. Therefore, the rapid detection of plant pathogens is critically important for preventing the rapid development of plant diseases and minimizing crop damage. Raman spectroscopy (RS) has been shown to be effective for detecting living biological samples. Compared with traditional detection methods, RS is fast, sensitive, and non-destructive; it also does not require sample labeling. In this study, we used Laser tweezers Raman spectroscopy combined with convolutional neural networks to detect two closely related strains of bacteria, Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), exuded from bacteria-infected rice leaves. The accuracy of this technique was 97.5 %. For the application of RS in the field, we used the portable Raman spectrometer to detect mock-inoculated as well as Xoo- and Xoc-infected rice leaves at different disease courses. The identification accuracy via this technique was 87.02 % in the early stage, in which no obvious symptoms were apparent. This method also revealed spectral differences in rice leaves caused by the two bacteria, which could be leveraged for subsequent analysis of the molecular mechanism of infection. Our results indicate that RS is a promising approach for the early detection of bacterial diseases in rice in the field, as well as for in-depth single-cell analysis in laboratory settings.

Real-time field disease identification based on a lightweight model

In the field of agricultural technology, rapid and accurate diagnosis of crop diseases is crucial for ensuring crop health and yield. However, most methods for automatic diagnosis of plant diseases are primarily based on high-computational-performance server platforms, which limits their application in real-world environments. This paper establishes a crop disease dataset collected from real field scenarios, which is utilized for training, validating the proposed models, and enhancing model generalization in existing research. It provides an accurate and practical database for future research endeavors. The key innovation of this study lies in the development of a lightweight convolutional neural network architecture with high real-time capabilities, characterized by its high precision and efficiency. The network abandons traditional deep convolution, adopting partial convolution and point-wise convolution techniques instead. This approach significantly reduces computational complexity while maintaining high accuracy, making the model highly suitable for application in resource-constrained real-time environments. The proposed method achieved an accuracy of 99.04 % on the open-source PlantVillage dataset and 92.82 % accuracy on a dataset with complex backgrounds constructed. Additionally, this paper also investigates the deployment of the proposed method and other mature technologies on mobile platforms. The focus of this evaluation is to compare the real-time performance on devices with limited computational resources, thereby verifying the model’s applicability in the real world.

Seed coating with biocontrol bacteria encapsulated in sporopollenin exine capsules for the control of soil-borne plant diseases

Coating seeds with biocontrol agents represents an effective approach for managing soil-borne plant diseases. However, improving the viability of biocontrol microorganisms on the seed surface or in the rhizosphere remains a big challenge due to biotic and abiotic stresses. In this work, we developed a microbial seed coating strategy that uses sporopollenin exine capsules (SECs) as carriers for the encapsulation of the biofilm-like biocontrol bacteria. SECs was extracted from camellia bee pollen, and then characterized by Fourier Transform infrared spectroscopy (FTIR), elemental analysis and thermal gravity analysis (TG). The Paenibacillus polymyxa ZF129, a biocontrol bacterium, was introduced into SECs using the vacuum-incubation method and characterized by scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). Notably, the ZF129 cell formed a biofilm-like structure inside the SECs, which enhanced their tolerance to acidic stress. As a proof of concept, we applied ZF129-loaded SECs to coat pak choi seeds using a straightforward plate-shaking technique. The coated seeds demonstrated a high control efficacy of up to 60.46 % against clubroot disease. Overall, this study sheds light on the application of SECs as promising carrier for the encapsulation of biofilm-like biocontrol bacteria, further augmenting the biocontrol functionality of microbial seed coating.

Verticillium dahliae Elicitor VdSP8 Enhances Disease Resistance Through Increasing Lignin Biosynthesis in Cotton.

Verticillium wilt caused by the soil-borne fungus Verticillium dahliae Kleb., is a destructive plant disease that instigates severe losses in many crops. Improving plant resistance to Verticillium wilt has been a challenge in most crops. In this study, a V. dahliae secreted protein VdSP8 was identified and shown to activate hyper-sensitive response (HR) and systemic acquired resistance (SAR) to Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) and Botrytis cinerea in tobacco plants. We identified a β-glucosidase named GhBGLU46 as a cotton plant target of VdSP8. VdSP8 interacts with GhBGLU46 both in vivo and in vitro and promotes the β-glucosidase activity of GhBGLU46. Silencing of GhBGLU46 reduced the expression of genes involved in lignin biosynthesis, such as GhCCR4, GhCCoAOMT2, GhCAD3 and GhCAD6, thus decreasing lignin deposition and increasing Verticillium wilt susceptibility. We have shown that GhBGLU46 is indispensable for the function of VdSP8 in plant resistance. These results suggest that plants have also evolved a strategy to exploit the invading effector protein VdSP8 to enhance plant resistance.

In-field disease symptom detection and localisation using explainable deep learning: Use case for downy mildew in grapevine

Diseases and pests in agriculture significantly impact crop yield and quality. Downy mildew (Plasmopara viticola) is a particular noteworthy example in grapevines. Traditional detection methods are laborious, subjective and time-consuming. Consequently, a technological solution based on artificial intelligence, would provide higher levels of reproducibility and sampling. The aim of this work was to develop an interpretable, automated method for detection and localisation of plant disease symptoms under field conditions. Images of the grapevine canopy were taken in 14 commercial vineyard plots under a range of lightning conditions, including both static and on-the-go settings. The images were processed using a sliding window, classifying sub-images into areas with and without downy mildew. Transfer learning, fine-tuning and data augmentation were employed to automate the classification, comparing convolutional neural networks (CNNs) and vision transformers (ViT). Subsequently, the trained model was integrated into the sliding window to localise regions within the canopy images exhibiting symptoms of downy mildew. Model predictions were interpreted using explainable artificial intelligence (XAI) methods. The EfficientNetV2S model achieved an accuracy of 91 % and an F1-score of 0.92 when classifying image areas and an Intersection over Union (IoU) of 0.83 when locating symptomatic areas. This method showed promising results, enabling automatic and explainable detection and localisation of plant diseases in complex conditions. The straightforward labelling process facilitated adaptation to new conditions, making it suitable for different crops and diseases. Integration into mobile platforms could enhance disease management and reduce the spread of pathogens, making a significant advance in agricultural technology.

Multi-spectral image transformer descriptor classification combined with molecular tools for early detection of tomato grey mould

Accurate early detection of plant diseases is a critical milestone in the development of self-navigating robots for precision agriculture and still remains one of the most significant challenges. Plant pathogens, like Botrytis cinerea causing grey mould disease, pose significant threats to agriculture and food safety. This study focuses on the early detection of B. cinerea in tomato crops using Artificial Intelligence. Specifically, the Deep Learning (DL) YOLOv8 architecture was employed to apply single class segmentation on plant captures to extract the tomato leaves locational information. Each plant capture includes five images from hyperspectral wavelengths (at 460, 540, 640, 775 and 875 nm) and one RGB images. The leaf segmentation achieved 81.7 % Mean Average Precision (mAP) at Intersection over Union (IoU) threshold 0.5 (mAP50). Then the for each leaf segment, and array of descriptors is extracted through various Transformer models. Finally, the descriptors are classified through a KNN or an LSTM solution and the results of all the descriptors for each leaf from each of the Transformer models, for each of the six images of the capture, are ensembled to yield the class of each leaf. The best ensemble results were produced by only accumulating results from some of the Transformer and specifically the MaxViT-L, the ViT-B (P:16 × 16 – C), the VOLO (D:5) and the XCIT-L (L:24 – P:16 × 16), by only using the results provided by the images at the hyperspectral wavelengths at 540, 640 nm and the RGB image, and by applying the LSTM solution. The process achieved a 79.41 % classification accuracy and 71.12 % F1-Score for all classes. Additionally, early, rapid and accurate detection of grey mould at the early stages of the infection or at latent infections when symptoms are not visible was assessed by comparative qPCR-based methods (qRT-PCR) for fungal biomass estimation in plant tissues and the rest of this study's methodology. The findings of this study represent a significant advancement in crop disease detection and management, and highlight the potential for integrating these methods into on-site digital systems (robots, mobile apps, etc.) under real-world settings. The results, demonstrate the effectiveness of combining segmentation and transformer-based classification models for early and accurate detection of grey mould disease.