Plant Diseases Research Articles

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.

Insect Pollinivores of Sorghum bicolor and Plant Traits that Influence Visitation1

Abstract Grasses such as sorghum, (Sorghum bicolor (L.) Moench), are rarely considered as an important food resource for pollinators. Here, we report insects collecting or consuming pollen of sorghum over the 8-wk flowering period of a mapping population in Tifton, GA. We also examine the response of insects to plant morphological traits and disease damage and the ability of bumble bees (Bombus impatiens) to cross-pollinate sorghum. The most numerous insect observed collecting/consuming sorghum pollen was the hover fly Toxomerus politus Say (i.e., the maize calligrapher) followed by honey bees (Apis mellifera L.), lined earwigs (Doru taeniatum Dorhn), lovebugs (Plecia nearctica Hardy), southern carpenter bees (Xylocopa micans Lepeletier), common eastern bumble bees (Bombus impatiens Cresson), exotic stripetails (Allograpta exotica Wiedemann), margined soldier beetles (Chauliognathus marginatus F.), a signal fly (Rivellia sp. Robineau-Desvoidy), and a dusky-winged hover fly (Ocyptamus fuscipennis Say). Maximum flowering occurred at Week 3, which coincided with the maximum number of maize calligraphers, bumble bees, and honey bees observed. A positive linear relationship was seen between the number of flowering plots and the number of maize calligraphers and honey bees. The probability of observing a bee on a panicle increased as plant height increased, whereas the probability of observing a bee or hover fly decreased as plant disease percentage increased. These results suggest that inflorescence abundance, plant height, and plant disease impact bee visitation in sorghum. Furthermore, field-captured bumble bees successfully fertilized sorghum in a greenhouse study, suggesting that bumblebees can pollinate sorghum flowers in field conditions.

Diversity and Prevalence of Indigenous Soil Bacillus spp. in the Major Cabbage Growing Agroecological Zones of Uganda

Different species of genus Bacillus have been reported from different environments of the world. They are reported to play a role of soil fertility improvement, plant growth promotion and disease and pest management. Most of these reports on Bacillus species are from studies conducted outside Uganda and therefore information on the prevalence and diversity of bacillus species in Ugandan soils is scanty. This study aimed at determining the prevalence and diversity of Bacillus spp. isolated from the cabbage rhizosphere in the four major cabbage-growing agroecological zones of Uganda. The experiment was conducted in a laboratory at the College of Veterinary Medicine, Animal Resources and Biosafety Makerere University for morphological and biochemical identification of the Bacillus bacteria. DNA extraction and PCR were conducted at the College of Natural Resources Makerere University while sequencing was done at Macrogen laboratories in Korea and Inqaba Biotec in South Africa. Morphological, biochemical and genomic analyses revealed five Bacillus spp. (22 Bacillus strains) grouped as B. cereus, B. mycoides, B. thuringiensis, B. megaterium and B. bingmayongensis. B. cereus and B. megaterium were the most dominant and widely spread Bacillus spp. A phylogenetic tree indicated three major clads, showing that B. thuringiensis was closely related to B. cereus while B.bingmayongesis was closely related to B.megaterium. The B. mycoides were closely related to some B. cereus strains and B. bingmayongensis. The phylogenetic tree further showed that some Bacillus strains of the same species were distantly related. It was therefore concluded that most abundant and prevalent Bacillus spp. in Ugandan soils were B. cereus and B. megaterium. The presence and abundance of these bacillus species in the Ugandan soil presents an opportunity for soil scientists to innovatively manipulate them for use as biofertilizers and biopesticides for crop production and management. Such innovations would reduce the reliance of farmers on synthetic fertilizers that are pollutants to the environment and unhealthy to the users and consumers

Assessment of Physico-Chemical Properties of Biogas Slurry as an Organic Fertilizer for Sustainable Agriculture

Chemical fertilizers have been extensively used for growing crops and controlling plant diseases, but they pose potential hazards to the environment, soil health, plants, and people. The current world situation highlights the need to implement eco-friendly agricultural practices for sustainable crop production. Using environmentally friendly manure, such as biogas slurry, can help reduce the negative effects of chemical fertilizers. Biogas slurry is an efficient waste material and organic fertilizer, making it an ideal supplement for sustainable crop production and waste management. An experiment was conducted at IARI, New Delhi, to explore the nutrient potential of biogas slurry. The main objective of this study was to assess biogas slurry's physico-chemical characteristics and nutrient contents. Samples of biogas slurry were collected in three replications and analyzed using standard methods for macro and micronutrients. The data revealed that biogas slurry has a pH of 7.2-8.5, EC of 1.06 to 1.12 dS/m, and organic carbon content of 41.7 to 45.8%. In terms of fertility, it contains significant amounts of nitrogen (1.98-2.17%), phosphorus (0.97 to 1.15%), and potassium (1.98 to 2.17%). Additionally, biogas slurry contains micronutrients such as Zn (0.023-0.027 ppm), Cu (0.005-0.009 ppm), Fe (0.32-0.38 ppm), and Mn (0.089-0.094 ppm). Statistical analysis using ANOVA and Post Hoc tests indicated that the mean data values among all three replications do not differ significantly. Therefore, it can be concluded that the nutritive value of biogas slurry is sufficient to reduce the reliance on chemical fertilizers in agriculture. It represents an optimal long-term organic remedy for developing fertile soil, ensuring enduring agricultural productivity, and mitigating the negative environmental impacts associated with waste management.

Characterization of Bipolaris bicolor germination: effects of a physical factor on fungal adaptability.

Studies on physiological responses to stimuli from physical factors are essential for understanding the dynamics of the microorganisms and higly important for the management of plant diseases. Besides, the development of an epidemiological model for pathogen populations requires studying their physiological responses to physical stimuli. The objective of this study was to evaluate the germination dynamics of spores from six isolates of Bipolaris bicolor under effects of light at 25°C. Suspensions of 1.6 × 105 conidia mL- 1 from the B. bicolor isolates were inoculated onto Petri dishes containing agar-water culture medium and incubated in a BOD chamber under two physical conditions: (a) constant darkness and (b) constant light for five hours. The study was conducted in a completely randomized design, with a 6 × 2 factorial arrangement (six B. bicolor isolates and two physical conditions) and five replications. The length of the germ tube was measured hourly. The constant darkness resulted in higher mean germ tube growth for the pathogen; however, differences in the final germination percentage were found among the isolates. The isolate F-24-02 exhibited the highest germination adaptability to constant darkness, presenting the longest germ tube length.

Leaf-whorl inoculation with Sporisorium reilianum may overcome field resistance of maize.

Maize yield is threatened by increasing incidences of head smut disease caused by Sporisorium reilianum. To help breeders identify S. reilianum-resistant maize lines, the availability of efficient screening systems would be an advantage. Here we assessed maize lines with distinct levels of field resistance against head smut disease in greenhouse experiments using two different inoculation techniques. Addition of mixtures of mating-compatible sporidia to the soil at seedling stage of the plant did not lead to plant disease, and we could detect only marginal amounts of fungal DNA in apical meristems at eighteen days after sowing. Inoculation of the maize lines by leaf-whorl inoculation led to both high disease incidence and prominent levels of fungal DNA in apical meristems in all tested maize lines regardless of their field resistance levels. Thus, S. reilianum entering the plant via the leaf whorl can escape existing resistance mechanisms of currently known field-resistant maize lines. Since field-resistant lines are also resistant to inoculation via teliospore-contaminated soil, we propose teliospore addition to seeds at the time of sowing (rather than leaf-whorl inoculation of seedlings) combined with quantitative detection of fungal DNA in apical meristems, as an efficient screening procedure to discover field-resistant lines. However, screening maize plants for resistance against the leaf-whorl inoculation method might be promising for the discovery of novel resistance mechanisms needed to develop durably resistant maize lines.

Plant Disease Identification Based on Encoder–Decoder Model

Plant Disease Identification Based on Encoder–Decoder Model

Study on the Cordyceps militaris Cultivation in Differential Media and Substrates Formulation

The study was study on the Cordyceps militaris Cultivation in Differential Media in plant diseases and weeds laboratory, Faculty of Agriculture, National University of Laos. The experiment was conducted for six months. This is of study have two parts, part one to testing fibers of growing Cordyceps militaris in Differential Media and Substrates Formulation. The experiment was set up as Completely Randomized Design (CRD) layout was used with four replications and three treatments as they are: T1 PDA (control), T2 supplements one + Silk worm pupae, T3 supplements two + egg. To testing fibers of growing Cordyceps militaris in three kind differentials of twenty days had different significant (P<0.05), T2 have medium testing fibers of growing Cordyceps militaris with amount of 8.71 cm, by T3 is 8.32 cm, the lowest by T1 8.06 cm, and to notice T2 have dark orange in color colony, by T3 and T1 light color yellow. Part two to testing substrates and supplements Cordyceps militaris in three kind differentials to yield. The experiment was arranged in a Completely Randomized Design (CRD) layout was used with four replications and five treatments as they are: T1 Rice bean + potato dextrose broth PDB (control), T2 Rice rations + potato dextrose broth (PDB), T3 Rice bean + liquid broth Silk worm pupae 30 gram, T4 Rice bean + liquid broth Silk worm pupae 60 gram, T5 Rice rations + liquid broth Silk worm pupae 60 gram. The study was use twenty-four. The results found that there are different significant (P<0.05), T4 fibers of growing Cordyceps militaris by 5.93 days,by T5, T3and T2 is 6.25, 8.00 and 10.18 days respectively. By T1 is 10.00 days. The developmenting of fibers to substitute flower mushroom in 5 treatments. The results found that there are different significant (P<0.05), T5. The developmenting of fibers to substitute flower mushroom 22.18 days, by T2, T3 and T4 is 28.56, 25.06 and 21.81 days respectively. T1 is 29.31 days. However, weight flower mushroom T5 weight 27.25 gram/glass, by T4, T3 and T1 weight 26.88, 23.44 and 18.13 gram/glass respectively. By T2 weight 17.81 gram/glass.

Exogenous Application of dsRNA—Inducing Silencing of the Fusarium oxysporum Tup1 Gene and Reducing Its Virulence

Fusarium oxysporum is a widespread soil-borne fungal pathogen that can infect various plants, causing wilt and root rot diseases. The root rot disease of Atractylodes macrocephala caused by F. oxysporum is among the most serious diseases associated with continuous cropping, significantly hindering its sustainable development. In this study, we aimed to investigate the effect of exogenous application of double-stranded RNA (dsRNA) on silencing the F. oxysporum Tup1 gene to reduce its virulence and to evaluate its potential application in controlling root rot disease in A. macrocephala. The Tup1 gene was amplified from the F. oxysporum genome, and different lengths of Tup1-dsRNA were designed and synthesized. The uptake of dsRNA by the fungus was verified using Tup1-dsRNA labeled with fluorescein, and in vitro dsRNA treatment experiments were conducted to assess its impact on the growth and virulence of F. oxysporum. Additionally, Tup1-dsRNA was applied to the roots of A. macrocephala to evaluate its effectiveness in controlling root rot disease. The experimental results showed that F. oxysporum could effectively uptake exogenously applied Tup1-dsRNA, significantly reducing Tup1 gene expression. All lengths of Tup1-dsRNA inhibited fungal growth and caused morphological changes in the fungal hyphae. Further plant experiments and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) analysis indicated that Tup1-dsRNA treatment significantly reduced the incidence of root rot disease in A. macrocephala, which was supported by the reduction in peroxidase (POD) and catalase (CAT) enzyme activities, malondialdehyde (MDA) content, and proline (Pro) levels in treated root tissues. This study demonstrated that exogenous dsRNA could reduce the virulence of F. oxysporum by silencing the Tup1 gene and effectively mitigate the root rot disease it causes in A. macrocephala. The successful application of Tup1-dsRNA provided strong evidence for the potential of RNA interference (RNAi) technology in plant disease control. Future research could further optimize the design and application of dsRNA to enhance its practical value in agriculture.

Plant Leaf Disease Detection Using Xception Model

The traditional farming practices have been causing significant financial losses to farmers due to various reasons. However, the implementation of a modern, smart agricultural system utilizing machine learning techniques appears promising in safeguarding farmers and traders against these risks. This advanced system facilitates farmers in identifying common diseases through simple image recognition, employing a variety of image processing methods. Notably, the Convolutional Neural Network (CNN) algorithm stands out as an effective choice among these methods. Interestingly, among the available models, there has been limited utilization of the Xception model, and no comprehensive comparative study involving this model with different classifiers was found. To address this gap, a study was undertaken in two distinct approaches. Firstly, the Xception model demonstrated remarkable accuracy in detecting plant diseases, achieving an impressive 98.3 percent accuracy rate. In comparison, other classifiers such as logistic regression and three additional methods attained accuracy rates of 93 percent and 92 percent, respectively. Secondly, a comparative analysis was conducted on the top 12 papers out of a selection of 45 papers, each employing different methods. The Xception method once again proved to be effective in this context. Through these tests and review studies, the Xception method emerged as a reliable and superior choice. It is expected that this research will provide valuable insights for researchers and stakeholders, potentially guiding the development of new research initiatives in this field.

Advancements in dsRNA-based approaches: a comprehensive review on potent strategies for plant disease management

Advancements in dsRNA-based approaches: a comprehensive review on potent strategies for plant disease management

In vitro assessment of antifungal activity of cinnamon leaves extract against the Colletotrichum sp. causes of anthracnose on tomato

Tomato is a horticultural plant widely cultivated in Bali. Over the three years, the productivity and quality of this plant have tended to decrease due to pathogen-derived diseases, such as anthracnose caused by Colletotrichum sp. Until recently, control of this pathogenic fungus has relied on chemical-based fungicides, which lead to many long-term complications, including pathogen resistance, environmental pollution, the extinction of non-target microorganisms, and negative impacts on human health. Therefore, alternative methods for plant disease control are urgently needed to combat these pathogen attacks. The use of plant-derived active compounds has been intensively researched worldwide as a more environmentally friendly alternative. The main objective of this research was to investigate the effectiveness of Cinnamomum burmanii acetone extract in inhibiting the growth of Colletotrichum sp., the causative agent of anthracnose in tomatoes, through an in vitro approach. A non-factorial randomized complete design was applied in the experiment. The results showed that the crude extract of cinnamon leaves inhibited the growth of the Colletotrichum sp. with an MIC value of 0.9%, an inhibition zone of 2.55 mm, and an optimal inhibitory concentration of 2%, producing an inhibition zone of 11.10 mm. A GC-MS analysis was conducted to identify the active compounds in the cinnamon leaf extract. Sixteen active compounds were identified, nine of which are known to have antimicrobial activity.

Traditional Strategies and Cutting-Edge Technologies Used for Plant Disease Management: A Comprehensive Overview

Agriculture plays a fundamental role in ensuring global food security, yet plant diseases remain a significant threat to crop production. Traditional methods to manage plant diseases have been extensively used, but they face significant drawbacks, such as environmental pollution, health risks and pathogen resistance. Similarly, biopesticides are eco-friendly, but are limited by their specificity and stability issues. This has led to the exploration of novel biotechnological approaches, such as the development of synthetic proteins, which aim to mitigate these drawbacks by offering more targeted and sustainable solutions. Similarly, recent advances in genome editing techniques—such as meganucleases (MegNs), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) and clustered regularly interspaced short palindromic repeats (CRISPR)—are precise approaches in disease management, but are limited by technical challenges and regulatory concerns. In this realm, nanotechnology has emerged as a promising frontier that offers novel solutions for plant disease management. This review examines the role of nanoparticles (NPs), including organic NPs, inorganic NPs, polymeric NPs and carbon NPs, in enhancing disease resistance and improving pesticide delivery, and gives an overview of the current state of nanotechnology in managing plant diseases, including its advantages, practical applications and obstacles that must be overcome to fully harness its potential. By understanding these aspects, we can better appreciate the transformative impact of nanotechnology on modern agriculture and can develop sustainable and effective strategies to mitigate plant diseases, ensuring enhanced agricultural productivity.

In vitro co-culture system for investigating Armillaria root rot in Prunus spp. using a fiber-supported liquid approach.

In vitro co-culture techniques that allow the growth of plants and pathogens under controlled environmental conditions are being used to re-create host plant infection. These approaches reduce infection times, promote reproducibility, and enable a rapid evaluation of plant-pathogen interactions. As a result, these systems have become essential in breeding programs aimed at developing plant resistance to diseases. In this study, we developed and validated an in vitro co-culture system to investigate the Armillaria root rot (ARR) affecting Prunus spp. This disease, caused by fungi Armillaria spp. and Desarmillaria caespitosa, poses a severe threat to the stone and nut fruit industry due to the susceptibility of most commercial rootstocks to infection and the lack of effective management options for its control. The system consists of a fiber-supported liquid approach in sterile plastic vessels that allows a fast and reproducible fungal infection under controlled environmental conditions. The floor of the vessels was covered with a polyester-fiber matte and a germination paper that served as an interface between the mycelia and the plant roots. The vessels were subjected to inoculation with Armillaria mellea and D. caespitosa, and three Prunus genotypes ('Guardian®', 'MP-29', and Prunus cerasifera '14-4') were co-cultured with both fungi. Disease progression and plant and fungal biomass were monitored during co-culture. The presented in vitro co-culture approach facilitates the concurrent growth of Armillaria/Desarmillaria spp. and Prunus spp., excluding most of the limitations associated with greenhouses and field experiments. This system provides consistent and reproducible conditions for investigating a prominent plant disease affecting Prunus spp.