Plant Diseases Research Articles

Comparative and Spatial Transcriptome Analysis of Rhododendron decorum Franch. During the Flowering Period and Revelation of the Plant Defense Mechanism

Background: Rhododendron is a globally distributed and extensive genus, comprising over 1000 species. In the southwestern mountains of China, there exists a remarkable diversity of Rhododendron, with Yunnan Province alone harboring more than 600 species. R. decorum Franch. has long been utilized by local communities for its medicinal and edible properties. However, the transcriptional regulation function, medicinal properties, and edibility characteristics of R. decorum Franch. currently lack a solid theoretical basis. Methods: Total RNA was extracted from leaves, corollas and androecium/gynoecium of R. decorum Franch. in Heqing county, followed by the construction of cDNA libraries and the de novo assembly of transcriptomes. Results: A total of 63,050 unigenes were extracted from the flowers and leaf organs of R. decorum Franch. Among these unigenes, 43,517 were predicted to be coding sequences, with 32,690 being effectively annotated. Differential gene expression enrichment was observed among different organs within their respective transcriptomes; notably floral organs exhibited significant defense against plant diseases along with signal transduction functions. Furthermore, during the flower harvesting period, all floral organs exhibited gene enrichment pathways associated with carbohydrate metabolism. Additionally, the stamen and pistil displayed flavonoid metabolism pathways, suggesting their potential applications as functional food or medicine. Conclusions: Our results shed light on plant–pathogen defense mechanisms and the molecular bias of flavonoids biosynthesis on flower organs during the flowering period, which might help to understand the consumption of R. decorum Franch. corollas by the Bai nationality of Heqing county.

The role of the rhizobiome recruited by root exudates in plant disease resistance: current status and future directions.

Root exudates serve as a bridge connecting plant roots and rhizosphere microbes, playing a key role in influencing the assembly and function of the rhizobiome. Recent studies have fully elucidated the role of root exudates in recruiting rhizosphere microbes to enhance plant performance, particularly in terms of plant resistance to soil-borne pathogens; however, it should be noted that the composition and amount of root exudates are primarily quantitative traits regulated by a large number of genes in plants. As a result, there are knowledge gaps in understanding the contribution of the rhizobiome to soil-borne plant disease resistance and the ternary link of plant genes, root exudates, and disease resistance-associated microbes. Advancements in technologies such as quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS) offer opportunities for the identification of genes associated with quantitative traits. In the present review, we summarize recent studies on the interactions of plant and rhizosphere microbes through root exudates to enhance soil-borne plant disease resistance and also highlight methods for quantifying the contribution of the rhizobiome to plant disease resistance and identifying the genes responsible for recruiting disease resistance-associated microbes through root exudates.

Insect Lipid Metabolism in the Presence of Symbiotic and Pathogenic Viruses and Bacteria.

Insects, like most animals, have intimate interactions with microorganisms that can influence the insect host's lipid metabolism. In this chapter, we describe what is known so far about the role prokaryotic microorganisms play in insect lipid metabolism. We start exploring microbe-insect lipid interactions focusing on endosymbionts, and more specifically the gut microbiota that has been predominantly studied in Drosophila melanogaster. We then move onto an overview of the work done on the common and well-studied endosymbiont Wolbachia pipientis, also in interaction with other microbes. Taking a slightly different angle, we then look at the effect of human pathogens, including dengue and other viruses, on the lipids of mosquito vectors. We extend the work on human pathogens and include interactions with the endosymbiont Wolbachia that was identified as a natural tool to reduce the spread of mosquito-borne diseases. Research on lipid metabolism of plant disease vectors is up and coming and we end this chapter by highlighting current knowledge in that field.

Precision management of Fusarium fujikuroi in rice through seed coating with an enhanced nanopesticide using a tannic acid-ZnIIformulation.

Seed coating with fungicides is a common practice in controlling seed-borne diseases, but conventional methods often result in high toxicity to plants and soil. In this study, a nanoparticle formulation was successfully developed using the metal-organic framework UiO-66 as a carrier of the fungicide ipconazole (IPC), with a tannic acid (TA)-ZnII coating serving as a protective layer. The IPC@UiO-66-TA-ZnII nanoparticles provided a controlled release, triggered and regulated by environmental factors such as pH and temperature. This formulation efficiently controlled the proliferation of Fusarium fujikuroi spores, with high penetration into both rice roots and fungal mycelia. The product exhibited high antifungal activity, achieving control efficacy rates of 84.09% to 93.10%, low biotoxicity, and promoted rice growth. Compared to the IPC flowable suspension formula, IPC@UiO-66-TA-ZnII improved the physicochemical properties and enzymatic activities in soil. Importantly, it showed potential for mitigating damage to beneficial soil bacteria. This study provides a promising approach for managing plant diseases using nanoscale fungicides in seed treatment.

Discovery, Biosynthesis, Total Synthesis, and Biological Activities of Solanapyrones: [4 + 2] Cycloaddition-Derived Polyketides of Fungal Origin.

Solanapyrones are metabolites bearing a 3,4-dehydrodecalin moiety isolated from cultures of different fungi that are associated with plant diseases. Research on solanapyrones resulted in the first report of a Diels-Alderase enzyme implicated in natural product biosynthesis related to the formation of the 3,4-dehydrodecalin core. In addition, several total syntheses of solanapyrones have been reported, which are also connected with the formation of the characteristic cycloaddition-derived 3,4-dehydrodecalin moiety. This Review provides the first comprehensive overview on the chemistry, biosynthesis, and biological activities of solanapyrones under the theme of synthetic and biosynthetic research progress on cycloaddition-derived secondary metabolites.

Emerging Nanochitosan for Sustainable Agriculture

Chemical-intensive agriculture challenges environmental sustainability and biodiversity and must be changed. Minimizing the use of agrochemicals based on renewable resources can reduce or eliminate ecosystems and biodiversity threats. Nanochitosan as a sustainable alternative offers promising solutions for sustainable agricultural practices that work at multiple spatial and temporal scales throughout the plant growth cycle. This review focuses on the potential of nanochitosan in sustainable agricultural production and provides insights into the mechanisms of action and application options of nanochitosan throughout the plant growth cycle. We emphasize the role of nanochitosan in increasing crop yields, mitigating plant diseases, and reducing agrochemical accumulation. The paper discusses the sources of nanochitosan and its plant growth promotion, antimicrobial properties, and delivery capacity. Furthermore, we outline the challenges and prospects of research trends of nanochitosan in sustainable agricultural production practices and highlight the potential of nanochitosan as a sustainable alternative to traditional agrochemicals.

Plant Disease Classification Using Convolutional Neural Networks

The agricultural sector faces significant losses due to plant diseases, particularly in major crops such as potatoes, tomatoes, and bell peppers. This paper presents a machine learning-based approach to classify diseases in these crops using leaf images. A Convolutional Neural Network (CNN) model was constructed and trained on datasets of healthy leaf images and diseased leaf images from potato, tomato, and bell pepper plants. The model successfully classifies diseases such as Bacterial Spot (for bell peppers), Early Blight, Late Blight, Mosaic Virus, Leaf Mold (for tomatoes), and with a classification accuracy of 93%, this system provides early detection, helping farmers take timely action to reduce disease impact and increase crop yield.

Robust Multi-Class Classification for Real-Time Agricultural Applications Using Efficient and Adaptive Deep Learning

Plant diseases severely affect agricultural productivity, necessitating accurate and rapid detection methods. This research presents a robust, multi-class plant disease classification framework using adaptive deep learning. We utilize pre-trained convolutional neural networks (CNNs), specifically Xception, InceptionResNetV2, InceptionV3, ResNet50, and the proposed EfficientNetB3-based Adaptive Augmented Deep Learning (EfficientNetB3-AADL) model. Our approach leverages transfer learning combined with extensive data augmentation and trimming techniques to enhance model performance and mitigate overfitting. The EfficientNetB3-AADL architecture incorporates convolutional and max pooling layers, regularization strategies, and a dense feature learning layer, optimized to classify 52 disease categories from a publicly available leaf image dataset. The model’s performance is extensively evaluated using metrics such as accuracy, precision, recall, and F1 score. Notably, EfficientNetB3-AADL achieves superior accuracy over 98%, outperforming other CNN models. The proposed methodology highlights the efficacy of compound scaling and adaptive data augmentation in ensuring robust and efficient disease classification, suitable for real-time agricultural applications. This advancement supports sustainable farming by offering a scalable, computationally efficient solution for early and accurate disease detection in diverse crop species.

Crucial role of fungi in environmentally sustainable agriculture

To produce more, better and safer food we must be able to do so while promoting the sustainable use of agricultural resources. The increase in agricultural production is currently through the use of pesticides, fertilizers, and developments in plant breeding and genetic skills. In naturally existing ecosystems, rhizospheric soils have biological living beings to favor the plant development, nutrient assimilation, stress tolerance, disease deterrence, carbon seizing and others. Fungi are among the most widely distributed organisms on Earth and are of great environmental importance. Mycorrhizal fungi, bacteria, actinomycetes, etc. solubilize nutrients and assist the plants in uptake by roots. Amongst them, vesicular/arbuscular mycorrhizal fungi (AMF) have key roles in the natural ecosystem, as the majority of the terrestrial plants form association with AMF. This symbiosis confers benefits directly to the host plant’s growth and development through the acquisition of phosphorus (P) and other mineral nutrients from the soil. They may also enhance the protection of plants against pathogens, increase plant diversity and have the potential to enhance plant growth under stressful environments. In this article, the sustainability and environmental aspects of agriculture, and the basic concepts of fungi will be reviewed to justify our point of view about the tremendous importance of fungi towards an environmentally sustainable agriculture.

Structural variation in the pangenome of wild and domesticated barley.

Pangenomes are collections of annotated genome sequences of multiple individuals of a species1. The structural variants uncovered by these datasets are a major asset to genetic analysis in crop plants2. Here we report a pangenome of barley comprising long-read sequence assemblies of 76 wild and domesticated genomes and short-read sequence data of 1,315 genotypes. An expanded catalogue of sequence variation in the crop includes structurally complex loci that are rich in gene copy number variation. To demonstrate the utility of the pangenome, we focus on four loci involved in disease resistance, plant architecture, nutrient release and trichome development. Novel allelic variation at a powdery mildew resistance locus and population-specific copy number gains in a regulator of vegetative branching were found. Expansion of a family of starch-cleaving enzymes in elite malting barleys was linked to shifts in enzymatic activity in micro-malting trials. Deletion of an enhancer motif is likely to change the developmental trajectory of the hairy appendages on barley grains. Our findings indicate that allelic diversity at structurally complex loci may have helped crop plantsto adapt to new selective regimes in agricultural ecosystems.

A small antimicrobial peptide derived from a Burkholderia bacterium exhibits a broad-spectrum and high inhibiting activities against crop diseases.

Crop diseases cause significant quality and yield losses to global crop products each year and are heavily controlled by chemicals along with very limited antibiotics composed of small molecules. However, these methods often result in environmental pollution and pest resistance, necessitating the development of new bio-controlling products to mitigate these hazards. To identify effective antimicrobial peptides (AMPs) considered as potential sources of future antibiotics, AMPs were screened from five bacterial strains showing antagonism against a representative phytopathogenic fungus (Rhizoctonia Solani) through the Bacillus subtilis expression system, which has been developed for identifying bacterial AMPs by displaying autolysis morphologies. A total of 5000 colonies were screened, and five displaying autolysis morphologies showed antagonism against R. solani. A novel AMP with the strongest antagonism efficiency was determined and tentatively named HR2-7, which is composed of 24 amino acids with an alpha-helical structure. HR2-7 has strong and broad-spectrum antimicrobial activity, tested against 10 g-positive and -negative bacteria and four phytopathogenic fungi by contact culture in plates with minimal lethal concentrations of 4.0 μM. When applied as purified peptide or in fermented B. subtilis culture solution, HR2-7 showed strong controlling efficiency on plants against diverse fungal and bacterial pathogens. Based on current understanding, HR2-7 is recognized as the first AMP derived from an agricultural antagonistic bacterium. It exhibits wide-ranging and notable antimicrobial efficacy, offering a supplementary approach for managing plant diseases, in addition to conventional chemical pesticides and antibiotics.

Photosynthesis Responses to the Infection with Plant Pathogens.

Photosynthesis, the remarkable process by which green plants synthesize nutrients using light energy, plays a crucial role in sustaining life on Earth. However, the effects of pathogens on photosynthesis are not widely understood. In general, a reduction of photosynthesis occurs upon the infection with pathogens. Two main scenarios are responsible for the reduction in photosynthetic capacity. In the first scenario, the pathogen attacks green aerial tissues such as caused by fungal and bacterial leaf spots and blights which affect photosynthesis by destroying green leaf tissue or causing defoliation. This leads to a decrease in the photosynthetic area, ultimately reducing photosynthesis. Interestingly, even when the overall chlorophyll content of leaves is significantly reduced due to pathogen invasion, the remaining chlorophyll-containing leaf area may maintain or even enhance its photosynthetic efficiency. This compensatory mechanism helps mitigate the loss of photosynthetic area. However, the overall yield of the plant is still affected. The second scenario is a reduction in chlorophyll content due to chlorosis, which is characterized by yellowing of leaves. It is a common symptom of plant diseases. It refers to a reduction in the amount of chlorophyll per chloroplast, rather than a decrease in chloroplast number. Diseases caused by viruses and phytoplasmas often exhibit chlorosis. While pathogens disrupt photosynthesis, plants exhibit significant adaptations to cope with these challenges. Understanding these interactions is essential for sustainable agriculture and ecosystem health. Thus, in this review, we discuss the effect of several pathogens on the photosynthesis processes and efficiency in detail.

DeepEMPR: coffee leaf disease detection with deep learning and enhanced multivariance product representation

Plant diseases threaten agricultural sustainability by reducing crop yields. Rapid and accurate disease identification is crucial for effective management. Recent advancements in artificial intelligence (AI) have facilitated the development of automated systems for disease detection. This study focuses on enhancing the classification of diseases and estimating their severity in coffee leaf images. To do so, we propose a novel approach as the preprocessing step for the classification in which enhanced multivariance product representation (EMPR) is used to decompose the considered image into components, a new image is constructed using some of those components, and the contrast of the new image is enhanced by applying high-dimensional model representation (HDMR) to highlight the diseased parts of the leaves. Popular convolutional neural network (CNN) architectures, including AlexNet, VGG16, and ResNet50, are evaluated. Results show that VGG16 achieves the highest classification accuracy of approximately 96%, while all models perform well in predicting disease severity levels, with accuracies exceeding 85%. Notably, the ResNet50 model achieves accuracy levels surpassing 90%. This research contributes to the advancement of automated crop health management systems.

Climate change and plant pathogens: Understanding dynamics, risks and mitigation strategies

AbstractClimate change is reshaping the interactions between plants and pathogens, exerting profound effects on global agricultural systems. Elevated tropospheric ozone levels due to climate change hinder plant photosynthesis and increase vulnerability to biotic invasion. The prevailing atmospheric conditions, including temperature and humidity, profoundly influence fungal pathogenesis, as each stage of a pathogen's life cycle is intricately linked to temperature variations. Likewise, climate change alters bacterial behaviour, fostering increased production of extracellular polysaccharides by plant‐pathogenic bacteria in warmer temperatures. Heat‐adapted bacteria, such as Burkholderia glumea and Ralstonia solanacearum, are emerging as significant global threats as temperature rise. Viruses, too, respond dynamically to climate shifts, with certain species favouring warmer climates for replication, resulting in expanded geographical ranges and modified transmission patterns. Nematodes, formidable constraints in crop production, exhibit temperature‐dependent life cycles and would have potentially accelerated proliferation and distribution as global warming progresses. Molecular‐level changes in pathogenesis, induced by temperature fluctuations, influence various pathogens, thereby impacting their virulence and interactions with host plants. Modelling studies predict changes in disease risks and distributions under future climate scenarios, highlighting the necessity of integrating climate data into crop disease models for accurate forecasts. Mechanistic and observational models illustrate pathogen behaviours amidst changing environmental conditions, providing crucial insights into future disease dynamics. In addition, controlled experiments study disease responses under simulated climate scenarios, underscoring the urgency for comprehensive research to devise effective resistance strategies against severe plant diseases.

An intelligent agriculture monitoring framework for leaf disease detection using YOLOv7

Agriculture is one of the most important economic sectors on which societies have relied since ancient times. With the recent development of technology, agriculture has also been incorporating modern techniques such as the Internet of Things and Artificial Intelligence to improve productivity and monitor the farming process. One of agriculture’s most prominent issues is the spread of plant diseases and the lack of real-time monitoring. Various systems and operations have recently been developed to predict and diagnose plant diseases. However, current operations have been selective, focusing on a specific aspect without addressing other important aspects, resulting in either partial or compound application of results, rendering the desired outcomes ineffective. To deal with such challenges, we propose an intelligent framework for real-time agriculture monitoring and disease detection, namely a system for monitoring plant diseases using YOLOv7. In the proposed framework, a rule-based policy has been designed for detecting plant diseases using online plant leaf monitoring, sensors, and surveillance cameras. Images of plant leaves captured by different cameras are sent in real-time to central cloud servers for disease detection. The improved YOLOv7 technology is utilized for plant disease detection, and the proposed system has been evaluated using a dataset of diseased tomato leaves, comparing it with different models based on various performance metrics to demonstrate its effectiveness, achieving an accuracy of 96%.

An attention-based deep network for plant disease classification

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method.

Calculation Method Based on Flower Petal Area and Other Plant Leaf Spot Area

In order to calculate the area of irregular shapes such as plant leaves and diseased spots or flower petals, this paper presents a new method to calculate them by using flood fill algorithm, HSV color space, improved k-means algorithm and morphological operation. First, 501 butterfly petal images and pathological leaf images of Bauhinia and phyllotaxus were collected, and then flood was used Fill algorithm selects the disease-free area and records the selected pixel value. HSV color space conversion is applied to the image to facilitate the segmentation of leaves. Then, the improved k-means algorithm is used to extract the binary image of leaves and record the pixel value of the outer contour with morphological closed operation. Finally, the proportion and truth of the disease spots of plant leaves are obtained by calculating the pixel value and the real value of the rectangle in the sampling area Real area. Compared with the results of artificial labeling, the average accuracy of petal area and lesion area of Phalaenopsis was 96.3% and 96.61%, respectively. It can be seen that the program can calculate the area of irregular shape of plant surface accurately. In conclusion, this method can replace the artificial grid method to calculate the information of plant leaf area and disease proportion, and effectively reduce the work intensity of experimental personnel.

New insights into the evolution and function of the UMAMIT (USUALLY MULTIPLE ACIDS MOVE IN AND OUT TRANSPORTER) gene family.

UMAMIT proteins have been known as key players in amino acid transport. In Arabidopsis, functions of several UMAMITs have been characterized, but their precise mechanism, evolutionary history and functional divergence remain elusive. In this study, we conducted phylogenetic analysis of the UMAMIT gene family across key species in the evolutionary history of plants, ranging from algae to angiosperms. Our findings indicate that UMAMIT proteins underwent a substantial expansion from algae to angiosperms, accompanied by the stabilization of the EamA (the main domain of UMAMIT) structure. Phylogenetic studies suggest that UMAMITs may have originated from green algae and be divided into four subfamilies. These proteins first diversified in bryophytes and subsequently experienced gene duplication events in seed plants. Subfamily I was potentially associated with amino acid transport in seeds. Regarding subcellular localization, UMAMITs were predominantly localized in the plasma membrane and chloroplasts. However, members from clade 8 in subfamily III exhibited specific localization in the tonoplast. These members may have multiple functions, such as plant disease resistance and root development. Furthermore, our protein structure prediction revealed that the four-helix bundle motif is crucial in controlling the UMAMIT switch for exporting amino acid. We hypothesize that the specific amino acids in the amino acid binding region determine the type of amino acids being transported. Additionally, subfamily II contains genes that are specifically expressed in reproductive organs and roots in angiosperms, suggesting neofunctionalization. Our study highlights the evolutionary complexity of UMAMITs and underscores their crucial role in the adaptation and diversification of seed plants.

Predictive Health Monitoring of Palm Trees: Techniques and Applications for Enhanced Processing

This paper presents new framework for predictive health monitoring in palm trees using advanced deep learning methods. The system then selects the YOLOv8 method for the detection of the health of palm trees and further divides them into their real-time classification. Here, the model will, after training on its dataset, classify the palm tree as healthy, diseased, and stressed trees, keeping all the influential factors of the growth of the palm tree in consideration. It makes provision for the profiling of health status by way of early intervention, reducing risks and preventing diseases with a view to optimizing crop yields within palm plantations. The proposed model leverages state-of-the-art methodologies in object detection to process images of the palm tree with identification of key indicators of health issues. This approach has great implications for agricultural productivity in view of the maintenance of plant health through early detection. Further, the paper discusses challenges presented during the training and validation of the model, their strategies for overcoming such obstacles, and goes further into relating details to the technical architecture of the model. Further details on the works related to plant disease detection, image-based classification, and further applications of deep learning in agriculture have inspired the research work. Based on the obtained results, it can be observed that the proposed system provides a scalable and efficient solution for continuous monitoring and assessment of the health conditions of palm trees, hence enabling sustainable agricultural practices.

Microbial Basis for Suppression of Soil-Borne Disease in Crop Rotation

The effect of crop rotation on soil-borne diseases is a representative case of plant–soil feedback in the sense that plant disease resistance is influenced by soils with different cultivation histories. This study examined the microbial mechanisms inducing the differences in the clubroot (caused by Plasmodiophora brassicae pathogen) damage of Chinese cabbage (Brassica rapa subsp. pekinensis) after the cultivation of different preceding crops. It addresses two key questions in crop rotation: changes in the soil bacterial community induced by the cultivation of different plants and the microbial mechanisms responsible for the disease-suppressive capacity of Chinese cabbage. Twenty preceding crops from different plant families showed significant differences in the disease damage, pathogen density, and bacterial community composition of the host plant. Structural equation modelling revealed that the relative abundance of four key bacterial orders in Chinese cabbage roots can explain 85% and 70% of the total variation in pathogen density and disease damage, respectively. Notably, the relative dominance of Bacillales and Rhizobiales, which have a trade-off relationship, exhibited predominant effects on pathogen density and disease damage. The disease-suppressive soil legacy effects of preceding crops are reflected in compositional changes in key bacterial orders, which are intensified by the bacterial community network.