Plant Diseases Research Articles

Evaluation of Antifungal Activity of Bacterial Endophytes Against Fusarium Oxysporum Causing Wilt Disease

Fusarium oxysporum is main causative agent behind the most destructive soil-borne plant disease, fusarium wilt, which damages economically significant food crops like bananas, sweet potatoes, tomatoes, legumes and melons. Traditionally, the disease is controlled by physical and chemical methods that affect the environment adversely. One of the ecofriendly approach of plant disease prevention is biological control. Plant growth promoting rhizobacteria and endophytes are now consider as promising agent for disease control. In the present study bulb of Allium sativum plant were used for isolation of endophytes and the endophytes were screened for their bio-control potential. Thirteen bacterial endophytes (ESS01, ESS02, ESS03, ESS06, ESS023, ESS024, ESS025, ESS072, ESS073, ESS074, ESS075, ESS076 and ESS077) exhibited antifungal activity against Fusarium oxysporum f. sp. ciceris. All the isolates inhibited more than 59% mycelial growth on dual-culture assay. However isolate ESS01 and ESS02 exhibited higher antifungal activity (74.81% & 71.48%) against the pathogen Fusarium oxysporum f. sp. ciceris. Only five endophytic isolates (ESS01, ESS02, ESS023, ESS076 & ESS077) out of thirteen endophytes were selected for the pot experiment because of having plant growth promoting attributes and inhibiting more than 70% mycelia growth of Fusarium oxysporum. Biocontrol technique of production of plant against harmful pathogen is now a promising approach for ecofriendly and sustainable agriculture. In this context endophytic isolates were analysed for their antifungal properties especially on Fusarium oxysporum. The endophytic isolates Bacillus aerius ESS02, Bacillus velezensis ESS076, and Bacillus siamensis ESS077 significantly enhanced the plant biomass in chick pea plant even in presence of pathogen and eliminating the harmful effect of Fusarium oxysporum on chick pea plant. Overall, present investigation indicate that Allium sativum harbors various endophytic bacteria that possess excellent antifungal activity and can be used as bio-control agent to control wilt disease of various plants.

Bioassay Study of Azadirachtin And Plumbagin on Pericallia ricini (Lepidoptera: Arctiidae)

In recent years, the overuse of commercially obtainable synthetic pesticides against phytophagous insects has augmented their bioaccumulation in the biosphere, foremost to increased resistance and reduced soil biodiversity. Moreover, extreme uses of insecticides enter various environmental resources as an outcome of overflow, initiating deleterious health problems to agriculturalists and consumers of agricultural products. Hence, more attention is being paid towards the expansion of substitute eco-friendly insecticides that will help in an effective pest management system and also prevent longstanding exposure that causes diseases. As a result, important ecologically friendly and harmless alternative practice strategies to artificial compounds are essential. Azadirachtin and plumbagin (phytoproducts) have been identified as an extraordinary biocontrol agent with low toxicity and high efficacy among many plant products for latent chemotherapeutic compounds in plant pest and disease management systems. The biocidal effectiveness of neem is attributed to its azadirachtin active ingredient, which affect some metabolic processes in insects such as protein synthesis, deviations in biological fitness, reduced sexual communication, and chitin synthesis. Similarly, Plumbagin (Napthaquinones) have established substantial attention in agricultural chemistry because of a novel action mode, extremely high activity against a broad spectrum of insects, low acute toxicity to mammals, and environmentally benign characteristics. The present study indicated that Azadirachtin and Plumbagin can be significant alternatives to Chemical insecticides for Pericallia ricini control in short-cycle crops.

Hollow Mesoporous Silica Nanoparticles as a New Nanoscale Resistance Inducer for Fusarium Wilt Control: Size Effects and Mechanism of Action.

In agriculture, soil-borne fungal pathogens, especially Fusarium oxysporum strains, are posing a serious threat to efforts to achieve global food security. In the search for safer agrochemicals, silica nanoparticles (SiO2NPs) have recently been proposed as a new tool to alleviate pathogen damage including Fusarium wilt. Hollow mesoporous silica nanoparticles (HMSNs), a unique class of SiO2NPs, have been widely accepted as desirable carriers for pesticides. However, their roles in enhancing disease resistance in plants and the specific mechanism remain unknown. In this study, three sizes of HMSNs (19, 96, and 406 nm as HMSNs-19, HMSNs-96, and HMSNs-406, respectively) were synthesized and characterized to determine their effects on seed germination, seedling growth, and Fusarium oxysporum f. sp. phaseoli (FOP) suppression. The three HMSNs exhibited no side effects on cowpea seed germination and seedling growth at concentrations ranging from 100 to 1500 mg/L. The inhibitory effects of the three HMSNs on FOP mycelial growth were very weak, showing inhibition ratios of less than 20% even at 2000 mg/L. Foliar application of HMSNs, however, was demonstrated to reduce the FOP severity in cowpea roots in a size- and concentration-dependent manner. The three HMSNs at a low concentration of 100 mg/L, as well as HMSNs-19 at a high concentration of 1000 mg/L, were observed to have little effect on alleviating the disease incidence. HMSNs-406 were most effective at a concentration of 1000 mg/L, showing an up to 40.00% decline in the disease severity with significant growth-promoting effects on cowpea plants. Moreover, foliar application of HMSNs-406 (1000 mg/L) increased the salicylic acid (SA) content in cowpea roots by 4.3-fold, as well as the expression levels of SA marker genes of PR-1 (by 1.97-fold) and PR-5 (by 9.38-fold), and its receptor gene of NPR-1 (by 1.62-fold), as compared with the FOP infected control plants. Meanwhile, another resistance-related gene of PAL was also upregulated by 8.54-fold. Three defense-responsive enzymes of POD, PAL, and PPO were also involved in the HMSNs-enhanced disease resistance in cowpea roots, with varying degrees of reduction in activity. These results provide substantial evidence that HMSNs exert their Fusarium wilt suppression in cowpea plants by activating SA-dependent SAR (systemic acquired resistance) responses rather than directly suppressing FOP growth. Overall, for the first time, our results indicate a new role of HMSNs as a potent resistance inducer to serve as a low-cost, highly efficient, safe and sustainable alternative for plant disease protection.

Detecting Three Different Diseases of Plants by Using CNN Model and Image Processing

Agricultural output is critical to Pakistan's economy, and plant disease detection is critical for both the environment and human health. This work presents a CNN-based approach for detecting plant diseases, which is evaluated on sample photos to assess the temporal complexity and infected area. The model was given three disease cases: corn common rust, tomato bacterial spot, and potato early blight. Using the CNN algorithm, the CNN model attained an accuracy of 95.55% for tomato bacterial spot, 96.72% for Corn Common Rust, and 97.63% for Potato Early Blight. This method may aid in the diagnosis and treatment of plant diseases.

Antibiotic Resistance in Plant Pathogenic Bacteria: Recent Data and Environmental Impact of Unchecked Use and the Potential of Biocontrol Agents as an Eco-Friendly Alternative.

The economic impact of phytopathogenic bacteria on agriculture is staggering, costing billions of US dollars globally. Pseudomonas syringae is the top most phytopathogenic bacteria, having more than 60 pathovars, which cause bacteria speck in tomatoes, halo blight in beans, and so on. Although antibiotics or a combination of antibiotics are used to manage infectious diseases in plants, they are employed far less in agriculture compared to human and animal populations. Moreover, the majority of antibiotics used in plants are immediately washed away, leading to environmental damage to ecosystems and food chains. Due to the serious risk of antibiotic resistance (AR) and the potential for environmental contamination with antibiotic residues and resistance genes, the use of unchecked antibiotics against phytopathogenic bacteria is not advisable. Despite the significant concern regarding AR in the world today, there are inadequate and outdated data on the AR of phytopathogenic bacteria. This review presents recent AR data on plant pathogenic bacteria (PPB), along with their environmental impact. In light of these findings, we suggest the use of biocontrol agents as a sustainable, eco-friendly, and effective alternative to controlling phytopathogenic bacteria.

Multiclass classification of diseased grape leaf identification using deep convolutional neural network(DCNN) classifier

The cultivation of grapes encounters various challenges, such as the presence of pests and diseases, which have the potential to considerably diminish agricultural productivity. Plant diseases pose a significant impediment, resulting in diminished agricultural productivity and economic setbacks, thereby affecting the quality of crop yields. Hence, the precise and timely identification of plant diseases holds significant importance. This study employs a Convolutional neural network (CNN) with and without data augmentation, in addition to a DCNN Classifier model based on VGG16, to classify grape leaf diseases. A publicly available dataset is utilized for the purpose of investigating diseases affecting grape leaves. The DCNN Classifier Model successfully utilizes the strengths of the VGG16 model and modifies it by incorporating supplementary layers to enhance its performance and ability to generalize. Systematic evaluation of metrics, such as accuracy and F1-score, is performed. With training and test accuracy rates of 99.18 and 99.06%, respectively, the DCNN Classifier model does a better job than the CNN models used in this investigation. The findings demonstrate that the DCNN Classifier model, utilizing the VGG16 architecture and incorporating three supplementary CNN layers, exhibits superior performance. Also, the fact that the DCNN Classifier model works well as a decision support system for farmers is shown by the fact that it can quickly and accurately identify grape diseases, making it easier to take steps to stop them. The results of this study provide support for the reliability of the DCNN classifier model and its potential utility in the field of agriculture.

Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images dataset

Performance evaluation and optimization of convolutional neural network architectures for Tomato plant disease eleven classes based on augmented leaf images dataset

Selfish Grower Behavior Can Group-optimally Eradicate Plant Diseases Caused by Coinfection

Selfish Grower Behavior Can Group-optimally Eradicate Plant Diseases Caused by Coinfection

Integrating 5G and machine learning technologies for advanced PDM in smart farming

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation.

Evaluation of biocontrol efficacy of trichoderma harzianum against fusarium oxysporium in tomatoes ( <i>Solanum esculentum L.</i>)

Utilizing biological techniques to manage plant diseases has demonstrated efficacy in fostering ecosystem sustainability and augmenting agricultural output and quality. A study was undertaken at the Department of Plant Science and Biotechnology, Imo State University, Owerri, Nigeria to evaluate the suppressive impact of T. harzianum on the proliferation of F. oxysporusm f. sp. lycopersici, and the advancement of F. oxysporum infection in tomato plants. The trial comprised culturing only T. harzianum in the Petri dish, culturing T. harzianum and F. oxysporum in the Petri dish (dual culture), and culturing only F. oxysporum in the petri dish, and with Mancozeb. The influence of T. harzianum on the advancement of F. oxysporum infection in tomato plants comprises of four distinct concentrations (1g, 2g, 3g, and 4g) of T. harzianum extract. The results revealed that the dual culture of T. harzianum and F. oxysporum successfully suppressed the mycelial growth of F. oxysporum. On the seventh day, the level of antagonistic activity exhibited by T. harzianum against F. oxysporum peaked at 9.05mm. Leaf yellowing and severe wilting, indicative of Fusarium wilt, were seen during the monitoring period. Tomato disease incidence and severity exhibited a consistent linear decline with increasing concentrations of T. harzianum. The disease severity reached its maximum level, (31.6%) on week four. Mancozeb treatment compared favorably with 4g T. harzianum in decreasing the mycelial growth of F. oxysporium, and reducing wilt incidence and severity. Therefore, T. harzianum can function as a biocontrol agent, providing a sustainable substitute for synthetic fungicides in the control ofFusariumoxysporium wilt disease.

Utilizing Machine Learning Techniques forthe Detection of Plant Leaf Diseases

Identification of plant diseases is crucial for preserving crops and ensuring food security. Analysis of detectable chemicals in plants is essential to understand transmission mechanisms and develop effective strategies for disease control measures to conserve agricultural products and prevent losses. However, manual monitoring of plant health is labor-intensive and time-consuming, requiring specialized skills and knowledge. To overcome these challenges, random forest systems are emerging as a powerful tool for disease detection and classification in plants. The process involves several steps, including image acquisition, preprocessing, and segmentation, followed by feature extraction, model training, and testing. Leveraging machine learning techniques, the random forest algorithm enables accurate classification of healthy and diseased leaves based on selected features. Image classification techniques are utilized to extract color information, while global features such as size and texture are captured through annotation. The dataset used for model training and testing comprises diverse samples, encompassing healthy and diseased plants. The random forest model is trained on 70% of the data to ensure robust learning, while the remaining 30% is reserved for testing, facilitating the exploration of model performance and overall feasibility

The Emerging Role of 2OGDs as Candidate Targets for Engineering Crops with Broad-Spectrum Disease Resistance.

Plant diseases caused by pathogens result in a marked decrease in crop yield and quality annually, greatly threatening food production and security worldwide. The creation and cultivation of disease-resistant cultivars is one of the most effective strategies to control plant diseases. Broad-spectrum resistance (BSR) is highly preferred by breeders because it confers plant resistance to diverse pathogen species or to multiple races or strains of one species. Recently, accumulating evidence has revealed the roles of 2-oxoglutarate (2OG)-dependent oxygenases (2OGDs) as essential regulators of plant disease resistance. Indeed, 2OGDs catalyze a large number of oxidative reactions, participating in the plant-specialized metabolism or biosynthesis of the major phytohormones and various secondary metabolites. Moreover, several 2OGD genes are characterized as negative regulators of plant defense responses, and the disruption of these genes via genome editing tools leads to enhanced BSR against pathogens in crops. Here, the recent advances in the isolation and identification of defense-related 2OGD genes in plants and their exploitation in crop improvement are comprehensively reviewed. Also, the strategies for the utilization of 2OGD genes as targets for engineering BSR crops are discussed.

An Automatic Monitoring System for Dragon Fruit Using Convolutional Neural Networks (CNN) and Internet of Things (IoT)

Plant diseases and pests have led to a decline in the quality of dragon fruit produced in Banyuwangi regency, Indonesia. Infections in dragon fruit cause rot, and farmers struggle to identify the pests responsible. To address this problem, this work proposed two concepts for the classification and monitoring systems of dragon fruit. The classification was done by processing some images of dragon fruits captured by DLSR camera and utilizes a convolutional neural network with three layers for training and testing. The monitoring system is based on the Internet of Things to tracks the status of ripe, raw, and rotten fruits. The application of the dragon fruit classification system to ripe, rotten, and raw fruits has yielded results that increase fold accuracy by 0.976, 0.981, and 0.986, respectively, with 200 training data in each of the three training and testing phases. There is a decrease of 0.024, 0.019, and 0.014 in fold loss accuracy. Meanwhile, the monitoring system's platform integrates the classification of dragon fruit to monitor the condition of ripe, raw, and rotting fruit in real time. With the implementation of the classification and monitoring system, farmers will be better equipped to predict when dragon fruit will ripen and prevent the spread of rot to other fruits.

Investigating attention mechanisms for plant disease identification in challenging environments

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field images present challenges due to complex backgrounds, similarities between different disease symptoms, and the need to detect multiple diseases simultaneously. These obstacles hinder the development of a reliable classification model. The attention mechanisms emerge as a critical factor in enhancing the robustness of classification models by selectively focusing on relevant regions or features within infected regions in an image. This paper provides details about various types of attention mechanisms and explores the utilization of these techniques for the machine learning solutions created by researchers for image segmentation, feature extraction, object detection, and classification for efficient plant disease identification. Experiments are conducted on three models: MobileNetV2, EfficientNetV2, and ShuffleNetV2, to assess the effectiveness of attention modules. For this, Squeeze and Excitation layers, the Convolutional Block Attention Module, and transformer modules have been integrated into these models, and their performance has been evaluated using different metrics. The outcomes show that adding attention modules enhances the original models' functionality.

Thaumatin-like Proteins in Legumes: Functions and Potential Applications-A Review.

Thaumatin-like proteins (TLPs) comprise a complex and evolutionarily conserved protein family that participates in host defense and several developmental processes in plants, fungi, and animals. Importantly, TLPs are plant host defense proteins that belong to pathogenesis-related family 5 (PR-5), and growing evidence has demonstrated that they are involved in resistance to a variety of fungal diseases in many crop plants, particularly legumes. Nonetheless, the roles and underlying mechanisms of the TLP family in legumes remain unclear. The present review summarizes recent advances related to the classification, structure, and host resistance of legume TLPs to biotic and abiotic stresses; analyzes and predicts possible protein-protein interactions; and presents their roles in phytohormone response, root nodule formation, and symbiosis. The characteristics of TLPs provide them with broad prospects for plant breeding and other uses. Searching for legume TLP genetic resources and functional genes, and further research on their precise function mechanisms are necessary.

SAD2 functions in plant pathogen Pseudomonas syringae pv tomato DC3000 defense by regulating the nuclear accumulation of MYB30 in Arabidopsis thaliana

Accurate nucleocytoplasmic transport of signal molecules is essential for plant growth and development. Multiple studies have confirmed that nucleocytoplasmic transport and receptors are involved in regulating plant disease resistance responses, however, little is known about the regulatory mechanism in plants. In this study, we showed that the mutant of the importin beta-like protein SAD2 exhibited a more susceptible phenotype than wild-type Col-0 after treatment with Pseudomonas syringae pv tomato DC3000 (Pst DC3000). Coimmunoprecipitation (Co-IP) and bimolecular fluorescence complementation (BiFC) experiments demonstrated that SAD2 interacts with the hypersensitive response (HR)-positive transcriptional regulator MYB30. Subcellular localization showed that MYB30 was not fully localized in the nucleus in sad2–5 mutants, and western-blot experiments further indicated that SAD2 was required for MYB30 nuclear trafficking during the pathogen infection process. A phenotypic test of pathogen inoculation demonstrated that MYB30 partially rescued the disease symptoms of sad2–5 caused by Pst DC3000, and that MYB30 worked downstream of SAD2 in plant pathogen defense. These results suggested that SAD2 might be involved in plant pathogen defense by mediating MYB30 nuclear trafficking. Taken together, our results revealed the important function of SAD2 in plant pathogen defense and enriched understanding of the mechanism of nucleocytoplasmic transport-mediated plant pathogen defense.

Research on the prevention and control of plant diseases by antagonistic microorganisms

Plant diseases, often caused by bacterial infections, pose significant challenges to human society due to their widespread and harmful consequences, resulting in substantial losses. Although traditional chemical prevention and control methods have shown effectiveness, they still pose serious environmental and other risks. Therefore, developing biological control is crucial for future plant disease prevention and management. In this study, we investigated the efficacy of antagonistic microorganisms. We conducted four related experiments and eventually reaffirmed the substantial inhibitory effect of antagonistic microorganisms on the growth of plant pathogens. Our findings are of great significance for further expanding the application scope and popularization of antagonistic bacteria and also enriching the specific application of antagonistic microorganisms for specific plant control.

Efficient Model on Corp Disease and Pest Detection with Deep Learning

Agricultural production faces significant challenges due to pests and diseases, causing substantial losses in crop yield globally. Traditional methods of pest and disease management are often manual, time-consuming, and prone to errors. In recent years, the integration of artificial intelligence (AI) techniques, particularly deep learning algorithms, with modern information and communication technology has shown promising results in addressing these challenges. This paper presents a comprehensive review of recent advancements in applying deep learning for detecting and classifying agricultural pests, diseases, and weeds. Various deep learning models, including convolutional neural networks (CNNs) such as Faster R-CNN, InceptionV3, DenseNet, and AlexNet, have been explored for their efficacy in identifying pests, diseases, and weeds in crops. While models like Faster R-CNN, InceptionV3, and DenseNet have demonstrated high accuracy rates ranging from 78.71% to 99.62% in classification tasks across different datasets and crops, the AlexNet architecture has also shown promising results in certain applications within agricultural image analysis. Additionally, the development of lightweight CNN architectures and the fusion of deep features with traditional handcrafted features have further enhanced the accuracy and efficiency of detection systems. Furthermore, the review discusses challenges and future research directions in the field, emphasizing the importance of large-scale datasets, model optimization, and real-time applications for practical implementation in agriculture. Overall, the findings highlight the potential of deep learning technologies, including models like AlexNet, in revolutionizing pest and disease management practices, leading to improved crop yield, food security, and sustainable agriculture. Keywords: Agricultural pests, plant diseases, deep learning, convolutional neural networks (CNNs), AlexNet, classification

Blast from the past: an historical perspective on plant diseases through the molecular and the data analytics looking glass

Blast from the past: an historical perspective on plant diseases through the molecular and the data analytics looking glass

AI-based smart agriculture 4.0 system for plant diseases detection in Tunisia

AI-based smart agriculture 4.0 system for plant diseases detection in Tunisia