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In the present study, an indoor potting experiment was conducted to study the effects of enhanced UV-B radiation and Magnaporthe oryzae on the growth, stomatal structure, photosynthesis, and endogenous hormone contents of a traditional rice cultivar Baijiaolaojing in the Yuanyang terraces of Yunnan Province. In addition, the relationships between these parameters and disease indices were analyzed. We aimed to clarify the response of the photosynthetic physiology of rice under the combined stress of UV-B radiation and M. oryzae. Compared with the M. oryzae infection treatment, all the treatments, including M. oryzae infection before (MBR), simultaneously with (MSR), and after (MAR) UV-B radiation significantly increased the rice height and biomass by 4%–11% and 30%–111%, respectively, and the stomatal structure and carotenoids content of leaves, while decreasing the contents of chlorophyll a and b, by 21%–41% and 63%–73%, respectively. Both the MSR and MBR treatments significantly increased the photosynthetic rate and transpiration rate of rice leaves. The MAR treatment weakened chlorophyll fluorescence parameters, including the actual photosystem II (PS II) photochemical efficiency, electron transport rate, photochemical quenching, and nonphotochemical quenching by 40%, 39%, 43%, and 24%, respectively. Moreover, the treatments of MAR, MSR, and MBR decreased the phytohormones content and the M. oryzae disease index by 27%–62% in rice leaves. Thus, the enhanced UV-B radiation contributed to suppressing the M. oryzae infection and alleviating its damage to the photosynthesis of rice leaves. This study is valuable for the control of rice blast fungus and offers important insights into plant pathology. [ABSTRACT FROM AUTHOR]

Plant diseases are a significant threat to modern agricultural productivity. Hydroponic systems are also affected for various reasons. Reliable and efficient detection methods are essential for early intervention and management of diseases in hydroponics. This study investigates the use of You Only Look Once (YOLO) models, namely YOLOv8 and YOLOv9, for the detection of plant diseases in a hydroponic environment. A diverse dataset was prepared, comprising images from a hydroponics system setup and the New Plant Disease Image Dataset from Kaggle. Custom annotated images were used to train and test the models and compare their accuracy, processing speed, and robustness in hydroponic systems. The results showed that YOLOv9 is slightly better than YOLOv8 in terms of detection accuracy, as it achieved 88.38% compared to 87.22%, respectively. YOLOv8 requires less computational resources and takes relatively less time than YOLOv9 for real-time plant disease detection. Therefore, it is recommended for portable devices. [ABSTRACT FROM AUTHOR]

The intensified concerns related to agrochemicals' ecological and health risks have encouraged the exploration of microbial agents as eco-friendly alternatives. Some members of Bacillus spp. are potential plant-growth-promoting agents and benefit numerous crop plants globally. This study aimed to explore the beneficial effects of two Bacillus strains (B. subtilis strain IS1 and B. amyloliquificiens strain IS6) capable of alleviating the growth of tomato plants against salinity stress and Fusarium wilt disease. These strains were able to significantly promote the growth of tomato plants and biomass accumulation in pot trials in the absence of any stress. Under salinity stress conditions (150 mM NaCl), B. subtilis strain IS1 demonstrated superior performance and significantly increased shoot length (45.74%), root length (101.39%), fresh biomass (62.17%), and dry biomass (49.69%) contents compared to control plants. Similarly, B. subtilis strain IS1 (63.7%) and B. amyloliquificiens strain IS6 (32.1%) effectively suppressed Fusarium wilt disease and significantly increased plant growth indices compared to the pathogen control. Furthermore, these strains increased the production of chlorophyll, carotenoid, and total phenolic contents. They significantly affected the activities of enzymes involved in antioxidant machinery and the phenylpropanoid pathway. Hence, this study effectively demonstrates that these Bacillus strains can effectively alleviate the growth of tomato plants under multiple stress conditions and can be used to develop bio-based formulations for use in the fields. [ABSTRACT FROM AUTHOR]

Managing plant diseases caused by phytopathogenic fungi, such as anthracnose caused by Colletotrichum species, is challenging. Different methods have been used to identify compounds with antibiotic properties. Trichoderma strains are a source of novel molecules with antifungal properties, including volatile organic compounds (VOCs), whose production is influenced by the nutrient content of the medium. In this study, we assessed the VOCs produced in dual confrontation systems performed in two culture media by Trichoderma strains (T. atroviride IMI206040, T. asperellum T1 and T3, and Trichoderma sp. T2) on Colletotrichum acutatum. We analysed the VOC profiles using gas chromatography coupled with mass spectrometry. The Luria Bertani (LB) medium stimulated the production of VOCs with antifungal properties in most systems. We identified 2-pentyl furan, dimethyl disulfide, and α-phellandrene and determined their antifungal activity in vitro. The equimolar mixture of those VOCs (250 µM ea.) resulted in 14% C. acutatum diametral growth inhibition. The infective ability and disease severity caused by the mycelia exposed to the VOCs mixture were notably diminished in strawberry leaves. Application of these VOCs as biofumigants may contribute to the management of anthracnose. LB represents a feasible strategy for identifying novel VOCs produced by Trichoderma strains with antifungal properties. [ABSTRACT FROM AUTHOR]

BACKGROUND: Succinate dehydrogenase inhibitor (SDHI) fungicides play important roles in the control of plant fungal diseases. However, they are facing serious challenges from issues with resistance and cross‐resistance, primarily attributed to their frequent application and structural similarities. There is an urgent need to design and develop SDHI fungicides with novel structures. RESULTS: Aiming to discover novel potent SDHI fungicides, 31 innovative pyrazole β‐ketonitrile derivatives with diphenyl ether moiety were rationally designed and synthesized, which were guided by a 3D‐QSAR model from our previous study. The optimal target compound A23 exhibited not only outstanding in vitro inhibitory activities against Rhizoctonia solani with a half‐maximal effective concentration (EC50) value of 0.0398 μg mL−1 comparable to that for fluxapyroxad (EC50 = 0.0375 μg mL−1), but also a moderate protective efficacy in vivo against rice sheath blight. Porcine succinate dehydrogenase (SDH) enzymatic inhibitory assay revealed that A23 is a potent inhibitor of SDH, with a half‐maximal inhibitory concentration of 0.0425 μm. Docking study within R. solani SDH indicated that A23 effectively binds into the ubiquinone site mainly through hydrogen‐bonds, and cation–π and π–π interactions. CONCLUSION: The identified β‐ketonitrile compound A23 containing diphenyl ether moiety is a potent SDH inhibitor, which might be a good lead for novel fungicide research and optimization. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Plant pathogens cause substantial crop losses annually, posing a grave threat to global food security. Fungicides have usually been used for their control, but the rapid development of pesticide resistance renders many ineffective, therefore the search for novel and efficient green pesticides to prevent and control plant diseases has become the top priority in crop planting. RESULTS: The results of bioassay studies indicated that most of the target compounds showed certain antimicrobial activity in vitro. In particular, compound X7 showed high inhibitory activity against Xanthomonas oryzae pv. oryzae (Xoo), with an EC50 value of 27.47 μg mL−1, surpassing conventional control agents such as thiazole zinc (41.55 μg mL−1) and thiodiazole copper (53.39 μg mL−1). Further studies on molecular docking showed that X7 had a strong binding affinity with 2FBW. The morphological change observed by scanning electron microscopy indicated that the surface of Xoo appears wrinkled and cracked under X7 treatment and a total of 2662 proteins were identified by label‐free proteomic analysis. Three experiments have elucidated the mechanism whereby X7 induced considerable changes in the physiological and biochemical properties of Xoo, which in turn affected the reproduction and growth of bacteria. CONCLUSION: This work represents a pivotal advancement, offering important reference for the research and development therapeutics in combating plant pathogens. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This article, titled "Measuring Heat Tolerance in a Sterilizing 'Anther-Smut' Pathogen of Wild Plants," explores the impact of increasing temperatures on the interaction between the herbaceous plant Silene latifolia and its fungal pathogen Microbotryum lychnidis-dioicae. The study investigates how temperature affects the growth, infection, and sporulation of the pathogen, as well as the survival and flowering of the host plant. The findings suggest that high temperatures suppress the life stages of the pathogen while not affecting the host, indicating a potential decline in the disease in the future. The article includes photographs illustrating the study and was published in the Bulletin of the Ecological Society of America. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners. [ABSTRACT FROM AUTHOR]

Medicinal plants have constituted a fundamental aspect of human health and wellness for millennia. The objective of this study was to document medicinal plants used to treat human and livestock ailments in the Tehuledere District. The data were collected using semistructured interviews, focus group discussions, and field observations with local informants. Preference ranking, direct matrix ranking, and informant consensus factor were used for data analysis. A total of 63 medicinal plant species belonging to 59 genera and 41 families were documented. The predominant families identified were Lamiaceae and Asteraceae, each containing 6 and 5 species, respectively. Of the recorded medicinal plants, 53 (80.95%) species were used for human ailments, whereas 12 (19.05%) species were used for animal health issues. Among the recorded medicinal plant species, shrubs constitute the highest number with 35% species. The most frequently used plant parts were leaves, accounting for 59% of remedies targeting human ailments. The administration of herbal treatments was primarily oral, aimed at addressing various diseases. The most significant threat to these medicinal plants was attributed to agricultural expansion, followed by the collection of firewood. The aim of documenting the use of medicinal plants in the treatment of diseases was to capture traditional practices, with species such as Eucalyptus globulus, Olea europaea subsp. cuspidata, and Lepidium sativum serving as the foundational basis for future pharmacological studies. It is imperative to prioritize the conservation of Laggera tomentosa and Urtica simensis to safeguard biodiversity and the cultural traditions associated with these endangered species. Engaging local communities in the management and conservation of plant resources, along with the preservation of their traditional knowledge, presents a cost‐effective and sustainable solution. [ABSTRACT FROM AUTHOR]

Precise and automatic plant disease detection represent a fundamental research topic. Indeed, traditional or manual disease detection can be laborious, inaccurate and time-consuming. In this work, a particular attention is given to automatic potato late blight disease detection. In fact, this devastating pathogen leads every year to a significant reduction in potato yields. In addition, since potatoes are a major food source for many nations, decreased production generate a real food insecurity. Therefore, considering these challenges, using advanced techniques in computer vision and machine learning allowed farmers to swiftly and accurately identify disease. The main objective of this research work is to generate a super-resolved labeled dataset (SRD) and evaluate its impact on plant disease detection. Three states of the art object detection methods (Faster-RCNN, Detr and Yolo V8) have been used to conduct an exhaustive evaluation on the effect of using a super-resolved dataset to perform detection. The obtained results show that the detection of potato late blight disease is enhanced using SRD. Training Yolo V8 model on SRD outperform other trained models in detecting very small lesions. In fact, training Yolo V8 on SRD reached higher mAP, lower Loss values and a reasonable inference time, making it suitable for real time applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A significant population of biocontrol microorganisms resides in the rhizosphere of plants, which can be utilized for plant disease control. To explore the potential of rhizosphere soil microorganisms as biocontrol agents against pepper blight, a bacterial strain Pa608 was screened from rhizosphere soil of pepper and identified as Pseudomonas aeruginosa through morphological characteristics and 16S rRNA sequences. The result showed that the strain Pa608 demonstrated antagonistic activity against Phytophthora capsici, effectively suppressing mycelial growth. The potted experiment showed a high control efficacy of 88.0%. Remarkably, the strain Pa608 also reduced the disease index of pepper blight in the field, resulting in control efficiencies of 74.9%. Moreover, the strain Pa608 also enhanced pepper plant height and yield. GC-MS analysis revealed the production of numerous secondary metabolites by the strain Pa608, with α-pinene displaying potent anti-oomycete activity by inhibiting P. capsici growth. In conclusion, P. aeruginosa Pa608 exhibited high biocontrol activity against P. capsici and can be utilized for the management of P. capsici in pepper cultivation. [ABSTRACT FROM AUTHOR]

Fungi play critical roles in the homeostasis of ecosystems globally and have emerged as significant causes of an expanding repertoire of devastating diseases in plants, animals, and humans. In this Commentary, we highlight the importance of fungal pathogens and argue for concerted research efforts to enhance understanding of fungal virulence, antifungal immunity, novel drug targets, antifungal resistance, and the mycobiota to improve human health. [ABSTRACT FROM AUTHOR]

Enzymes are molecules that play a crucial role in maintaining homeostasis and balance in all living organisms by catalyzing metabolic and cellular processes. If an enzyme's mechanism of action is inhibited, the progression of certain diseases can be slowed or halted, making enzymes a key therapeutic target. Therefore, identifying or developing enzyme inhibitors is essential for treating significant diseases and ensuring plant defense against pathogens. This review aims to compile information on various types of enzyme inhibitors, particularly those that are well studied and beneficial in both human and plant contexts, by analyzing their mechanisms of action and the resulting benefits. Specifically, this review focuses on three different types of enzyme inhibitors that are most studied, recognized, and cited, each with distinct areas of action and potential benefits. For instance, serine enzyme inhibitors in plants help defend against pathogens, while the other two classes—alpha-glucosidase inhibitors and carbonic anhydrase inhibitors—have significant effects on human health. Furthermore, this review is also intended to assist other researchers by providing valuable insights into the biological effects of specific natural or synthetic inhibitors. Based on the current understanding of these enzyme inhibitors, which are among the most extensively studied in the scientific community, future research could explore their use in additional applications or the development of synthetic inhibitors derived from natural ones. Such inhibitors could aid in defending against pathogenic organisms, preventing the onset of diseases in humans, or even slowing the growth of certain pathogenic microorganisms. Notably, carbonic anhydrase inhibitors have shown promising results in potentially replacing antibiotics, thereby addressing the growing issue of antibiotic resistance. [ABSTRACT FROM AUTHOR]

Bacterial virulence plays an important role in infection. Antibacterial virulence factors are effective for preventing crop bacterial diseases. Resin acid copper salt as an effective inhibitor exhibited excellent anti-Xanthomonas oryzae pv. oryzae (Xoo) activity with an EC50 of 50.0 μg mL−1. Resin acid copper salt (RACS) can reduce extracellular polysaccharides' (EPS's) biosynthesis by down-regulating gumB relative expression. RACS can also effectively inhibit the bio-mass of Xoo biofilm. It can reduce the activity of Xoo extracellular amylase at a concentration of 100 μg mL−1. Meanwhile, the results of virtual computing suggested that RACS is an enzyme inhibitor. RACS displayed good curative activity with a control effect of 38.5%. Furthermore, the result of the phytotoxicity assessment revealed that RACS exhibited slight toxicity compared with the control at a concentration of 200 μg mL−1. The curative effect was increased to 45.0% using an additional antimicrobial agent like orange peel essential oil. RACS markedly inhibited bacterial pathogenicity at a concentration of 100 μg mL−1 in vivo. [ABSTRACT FROM AUTHOR]

Accurate crop disease classification is crucial for ensuring food security and enhancing agricultural productivity. However, the existing crop disease classification algorithms primarily focus on a single image modality and typically require a large number of samples. Our research counters these issues by using pre-trained Vision–Language Models (VLMs), which enhance the multimodal synergy for better crop disease classification than the traditional unimodal approaches. Firstly, we apply the multimodal model Qwen-VL to generate meticulous textual descriptions for representative disease images selected through clustering from the training set, which will serve as prompt text for generating classifier weights. Compared to solely using the language model for prompt text generation, this approach better captures and conveys fine-grained and image-specific information, thereby enhancing the prompt quality. Secondly, we integrate cross-attention and SE (Squeeze-and-Excitation) Attention into the training-free mode VLCD(Vision-Language model for Crop Disease classification) and the training-required mode VLCD-T (VLCD-Training), respectively, for prompt text processing, enhancing the classifier weights by emphasizing the key text features. The experimental outcomes conclusively prove our method's heightened classification effectiveness in few-shot crop disease scenarios, tackling the data limitations and intricate disease recognition issues. It offers a pragmatic tool for agricultural pathology and reinforces the smart farming surveillance infrastructure. [ABSTRACT FROM AUTHOR]

Currently, the excessive use of pesticides has generated environmental pollution and harmful effects on human health. The controlled release of active ingredients through the use of nanomaterials (NMs) appears to reduce human exposure and ecosystem alteration. Although the use of NMs can offer an alternative to traditional methods of disease diagnosis and control, it is necessary to review the current approach to the application of these NMs. This review describes the most recent and significant advances in using NMs for diagnosing and treating plant diseases (bacteria, phytopathogenic fungi, viruses, and phytopathogenic nematodes) in cultivated plants. Most studies have focused on reducing, delaying, or eliminating bacteria, fungi, viruses, and nematodes in plants. Both metallic (including metal oxides) and organic nanoparticles (NPs) and composites are widely used in diagnosing and controlling plant diseases due to their biocompatibility and ease of synthesis. Few studies have been carried out with regard to carbon-based NPs due to their toxicity, so future studies should address the development of detection tools, ecological and economic impacts, and human health. The synergistic effect of NMs as fertilizers and pesticides opens new areas of knowledge on the mechanisms of action (plant–pathogen–NMs interaction), the interaction of NMs with nutrients, the effects on plant metabolism, and the traceability of NMs to implement sustainable approaches. More studies are needed involving in vivo models under international regulations to ensure their safety. There is still controversy in the release of NMs into the environment because they could threaten the stability and functioning of biological systems, so research in this area needs to be improved. [ABSTRACT FROM AUTHOR]

Chitin (Ct) is a crucial biopolymer present in fungi, algae, arthropods, and is usually obtained from crustacean shells. Chitosan (Cs) is a derivative from Ct deacetylation, and possesses numerous uses in various agro-industrial fields. Research on fungal-derived Ct and Cs is mostly focused on pharmaceutical uses, however their uses for plant disease control remain less explored. The main objective of the current study is to evaluate the possibility of using chitosan obtained from mushroom Pleurotus eryngii (Cs-Pe) for controlling some phytopathogens compared to commercial chitosan (C.Cs). This study is focused on the following key areas: (i) extracting Ct from P. eryngii mycelium and converting it to Cs through deacetylation, using both bleaching and non-bleaching methods; (ii) conducting a physico-chemical characterization and in vitro evaluation of the antimicrobial activity of the obtained Cs; (iii) performing an in vivo assessment of the phytotoxic and cytotoxic effects of Cs; and (iv) investigating in vivo the impact of the studied chitosan on fruit quality and its biocontrol efficacy against Monilinia laxa infections in plum fruits. Results showed that Cs-Pe, especially the unbleached one, displayed promising in vitro antimicrobial activity against the majority of tested pathogens. Regarding the cytotoxicity, the highest significant increase in cell abnormality percentage was observed in the case of C.Cs compared to Cs-Pe. In the in vivo study, Cs-Pe acted as a protective barrier, lowering and/or preventing moisture loss and firmness of treated plums. The studied Cs-Pe demonstrated notable efficacy against M. laxa which decreased the fruits' percentage decline. These results strongly suggest that Cs derived from P. eryngii is a potential candidate for increasing plums' shelf-life. This research shed light on the promising applications of P. eryngii-derived Cs in the agri-food field. [ABSTRACT FROM AUTHOR]

Bacterial wilt caused by Ralstonia solanacearum is one of the most important diseases in solanaceous plants, including peppers. It generally tends to be more serious under warm−temperature and moist (WM) conditions than at moist room−temperature (RM) conditions. Although immunity mechanisms at room temperature have been intensively studied, the mechanisms underlying WM conditions remain poorly understood. Herein, the pepper cysteine protease CaZingipain2 was expressed and functionally characterized in pepper immunity against R. solanacearum at WM conditions and at room temperature. The results showed that CaZingipain2 localized to the nucleus and was upregulated at the transcript level in pepper plants upon R. solanacearum infection under WM conditions (RSWM). Virus−induced gene silencing of CaZingipain2 significantly increased the susceptibility of pepper plants to RSWM, and was coupled with the downregulation of CaPRP1 and CaMgst3, which are specifically related to pepper immunity against RSWM, according to our previous studies, while its overexpression significantly reduced the susceptibility of N. benethamiana plants to RSWM compared to that of wild−type plants. In addition, our data showed that CaZingipain2 also acts positively in pepper immunity against R. solanacearum infection at room temperature by upregulating the SA− and JA−responsive PR genes, including CaNPR1 and CaDEF1. All these results indicate that CaZingipain2 improves pepper immunity against R. solanacearum under WM conditions and at room temperature by regulating different PR genes. [ABSTRACT FROM AUTHOR]

Featured Application: The SMARTerra decision support system provides high-resolution risk maps related to plant diseases and pests, enabling prompt and targeted intervention that minimizes environmental impact while minimizing economic losses. The prediction and monitoring of plant diseases and pests are key activities in agriculture. These activities enable growers to take preventive measures to reduce the spread of diseases and harmful insects. Consequently, they reduce crop loss, make pesticide and resource use more efficient, and preserve plant health, contributing to environmental sustainability. We illustrate the SMARTerra decision support system, which processes daily measured and predicted weather data, spatially interpolating them at high resolution across the entire Sardinia region. From these data, SMARTerra generates risk predictions for plant pests and diseases. Currently, models for predicting the risk of rice blast disease and the hatching of locust eggs are implemented in the infrastructure. The web interface of the SMARTerra platform allows users to visualize detailed risk maps and promptly take preventive measures. A simple notification system is also implemented to directly alert emergency responders. Model outputs by the SMARTerra infrastructure are comparable with results from in-field observations produced by the LAORE Regional Agency. The infrastructure provides a database for storing the time series and risk maps generated, which can be used by agencies and researchers to conduct further analysis. [ABSTRACT FROM AUTHOR]

Background: Poplar canker caused by Botryosphaeria dothidea is one of the most severe plant disease of poplars worldwide. In our study, we aimed to investigate the modes of antagonism by fermentation broth supernatant (FBS) of Streptomyces spiroverticillatus HS1 against B. dothidea. Results: In vitro, the strain and FBS of S. spiroverticillatus HS1 significantly inhibited mycelial growth and biomass accumulation, and also disrupted the mycelium morphology of B. dothidea. On the 3rd day after treatment, the inhibition rates of colony growth and dry weight were 80.72% and 52.53%, respectively. In addition, FBS treatment damaged the plasma membrane of B. dothidea based on increased electrical conductivity in the culture medium, and malondialdehyde content of B. dothidea mycelia. Notably, the analysis of key enzymes in glycolysis pathway showed that the activity of hexokinase (HK), phosphofructokinase (PFK), and pyruvate kinase (PK), Ca2+Mg2+-ATPase were significantly increased after FBS treatment. But the glucose contents were significantly reduced, and pyruvate contents were significantly increased in B. dothidea after treatment with FBS. Conclusions: The inhibitory mechanism of S. spiroverticillatus HS1 against B. dothidea was a complex process, which was associated with multiple levels of mycelial growth, cell membrane structure, material and energy metabolism. The FBS of S. spiroverticillatus HS1 could provide an alternative approach to biological control strategies against B. dothidea. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases Dataset, Corn Dataset, and Coffee Dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. [ABSTRACT FROM AUTHOR]

Yellow mosaic disease (YMD) caused by several begomoviruses is one of the major constraints of over a dozen leguminous crops worldwide, particularly in Asian and Southeast Asian countries. The present study aimed to investigate the distribution, diversity and prevalence of begomoviruses associated with YMD in leguminous hosts in five agro-climatic zones of India, to assess the extent of their geographical presence and develop location and crop-specific distribution maps. One hundred and seventy-four leguminous plant samples were tested from 32 locations in India to detect YMD-causing viruses. Additionally, publicly available data were incorporated into this study to provide a comprehensive overview of their distribution in India. This resulted in 581 reports on the DNA-A component representing 119 locations, which were also utilized to depict the distribution of YMD-causing viruses on a map of India. In this study, 117 full-length DNA-A and 103 DNA-B components were successfully characterized, representing the detected mungbean yellow mosaic India virus (MYMIV), mungbean yellow mosaic virus (MYMV), and horsegram yellow mosaic virus in the collected samples. Phylogenetic analysis of isolates of these species showed no differentiation based on location in India. Diversity indices revealed the abundance (55.9%) and dominance (0.56) of MYMIV across 119 locations. These findings hold significant implications for legume researchers, offering insights into disease prevalence and geographic distribution. Furthermore, the distribution of YMD-causing viruses in different agro-climatic zones will help researchers in developing zone-specific YMD-resistant cultivars of the legume crops and would facilitate effective disease management options. [ABSTRACT FROM AUTHOR]

Wheat is one of the most common staple crops and every year, a million tons have been exported from India worldwide. Among all states of India, more than 50% of wheat grew in different regions of Punjab. According to the Indian council of agricultural research ("Dare wheat annual report," n.d.), every year more than 10% growth rate of the wheat crop has decreased due to wheat rust diseases (WRD). Thus, the monitoring of the wheat crop diseases is important. This paper presents a panoptic segmentation-based hybrid approach namely as FERSPNET-50 approach for WRD detection in real-time images and a dataset was collected with different weather conditions in various regions of Punjab. With the help of the GNet model, the collected images have been classified into three classes of weather conditions and then this classified weather image has been fed as input to a faster region-based convolutional model (FRCNN). FRCNN model detects the wheat leaves and stems patches. As a part of the FRCNN training process, a semi-automatic annotation method is designed for generating ground truth rust lines. To further improve the accuracy of rust detections over the whole image, a pyramid scene parsing network has been employed to predict the rust diseases at the local level. Additionally, a deep CNN pretrained model has been utilized to determine the orientation of rust segments within each detected patch, and patches that don't support local level patches are viewed as false positives. After the classification of each patch, the amount of rust severity in wheat has been calculated. The experimental results show that the proposed approach has a high precision (0.97) as compared to state of art based YOLOV4(0.88) and RetinaNet (0.82) models for WRD detection. [ABSTRACT FROM AUTHOR]

Antimicrobial resistance in agriculture is a global concern that is growing every day. As a result, there is increasing interest in investigating different approaches to disease control in both plants and animals. Research on the identification and synthesis of antimicrobial compounds derived from plants and microbes has exploded in the last few years. The purpose of this review is to give a broad overview of the current knowledge regarding antimicrobials derived from plants and microbes and their possible uses in agricultural practises. It discusses a number of topics, such as the origins of these antimicrobial agents, how they work, and how well they work to fight infections in plants and animals. Furthermore, the difficulties pertaining to their execution and potential outcomes are examined. [ABSTRACT FROM AUTHOR]

Background: Plant-associated microorganisms can be found in various plant niches and collectively comprise the plant microbiome. The plant microbiome assemblages have been extensively studied, primarily in model species. However, a deep understanding of the microbiome assembly associated with plant health is still needed. Ginger rhizome rot has been variously attributed to multiple individual causal agents. Due to its global relevance, we used ginger and rhizome rot as a model to elucidate the metabolome-driven microbiome assembly associated with plant health. Results: Our study thoroughly examined the biodiversity of soilborne and endophytic microbiota in healthy and diseased ginger plants, highlighting the impact of bacterial and fungal microbes on plant health and the specific metabolites contributing to a healthy microbial community. Metabarcoding allowed for an in-depth analysis of the associated microbial community. Dominant genera represented each microbial taxon at the niche level. According to linear discriminant analysis effect size, bacterial species belonging to Sphingomonas, Quadrisphaera, Methylobacterium-Methylorubrum, Bacillus, as well as the fungal genera Pseudaleuria, Lophotrichus, Pseudogymnoascus, Gymnoascus, Mortierella, and Eleutherascus were associated with plant health. Bacterial dysbiosis related to rhizome rot was due to the relative enrichment of Pectobacterium, Alcaligenes, Klebsiella, and Enterobacter. Similarly, an imbalance in the fungal community was caused by the enrichment of Gibellulopsis, Pyxidiophorales, and Plectosphaerella. Untargeted metabolomics analysis revealed several metabolites that drive microbiome assembly closely related to plant health in diverse microbial niches. At the same time, 6-({[3,4-dihydroxy-4-(hydroxymethyl)oxolan-2-yl]oxy}methyl)oxane-2,3,4,5-tetrol was present at the level of the entire healthy ginger plant. Lipids and lipid-like molecules were the most significant proportion of highly abundant metabolites associated with ginger plant health versus rhizome rot disease. Conclusions: Our research significantly improves our understanding of metabolome-driven microbiome structure to address crop protection impacts. The microbiome assembly rather than a particular microbe's occurrence drove ginger plant health. Most microbial species and metabolites have yet to be previously identified in ginger plants. The indigenous microbial communities and metabolites described can support future strategies to induce plant disease resistance. They provide a foundation for further exploring pathogens, biocontrol agents, and plant growth promoters associated with economically important crops. 2Spd16sRC6jnqdSe2SRbtQ Video Abstract [ABSTRACT FROM AUTHOR]

Severe incidence of Alternaria spp. was observed in the three solanaceous vegetables viz. tomato, potato and brinjal grown in National Capital Region of Delhi and Haryana. The symptomatic plants exhibited necrotic brown lesions on the leaves initiating from the lower older leaves and extending to stem, affecting the whole plant. Subsequently, black sooty sporulation was observed in fruits of tomato and brinjal crop. During the survey, tomato crop was most susceptible with 98.99% incidence, followed by brinjal with 82.36% and potato with 61.19% incidence. The mean plant disease index varied between 9.98% and 65.99% among the three crops. Isolation and characterization of the causal agent from symptomatic plants revealed association of small-spored Alternaria spp. in all crops. Further, morphological and Internal Transcribed Spacer (ITS) sequencing phylogenetic analysis ascertained the fungus belong to Alternaria section of Alternaria genus. As tomato was found most susceptible, host plant resistance was explored among 197 tomato germplasm/breeding lines under epiphytotic conditions. It was found that 08 accessions exhibited moderate resistance against pathogen. The findings in the study suggest that small-spored Alternaria is an emerging problem in the solanaceous vegetables and warrants attention for effective management of the disease. [ABSTRACT FROM AUTHOR]

Long-term use of chemical fungicides to control plant diseases caused by fungi and oomycetes has led to pathogen resistance and negative impacts on public health and environment. There is a global search for eco-friendly methods and antagonistic bacteria are emerging as alternatives. We isolated a potent antagonistic bacterial strain (S1Bt23) from woodland soil in Québec, Canada. Taxonomic characterization by 16S rRNA, multi-locus sequence analysis, pairwise whole-genome comparisons, phylogenomics and phenotypic data identified strain S1Bt23 as a novel subspecies within Pseudomonas chlororaphis. In dual culture studies, strain S1Bt23 exhibited potent mycelial growth inhibition (60.2–66.7%) against Pythium ultimum. Furthermore, strain S1Bt23 was able to significantly bioprotect potato tuber slices from the development of necrosis inducible by P. ultimum. Annotations of the whole genome sequence of S1Bt23 revealed the presence of an arsenal of secondary metabolites including the complete phenazine biosynthetic cluster (phzABCDEFG). Thin-layer (TLC) and high-performance liquid (HPLC) chromatographic analyses of S1Bt23 extracts confirmed the production of phenazines, potent antifungal compounds. CRISPR/Cas9-mediated deletion of phzB (S1Bt23ΔphzB) or phzF (S1Bt23ΔphzF) gene abrogated phenazine production based on TLC and HPLC analyses. Also, S1Bt23ΔphzB and S1Bt23ΔphzF mutants lost antagonistic activity and bioprotection ability of potato tubers against P. ultimum. This demonstrated that phenazines are involved in the antagonistic activity of S1Bt23 against P. ultimum. Finally, based on genotypic and phenotypic data, we taxonomically conclude that S1Bt23 represents a novel subspecies for which the name Pseudomonas chlororaphis subsp. phenazini is proposed. [ABSTRACT FROM AUTHOR]

Apple tree canker is a serious disease caused by species of Cytospora. Xinjiang Uygur Autonomous Region is one of the most important apple-producing areas in China. However, losses due to apple Cytospora canker have seriously damaged the apple industry and affected the economic development of the apple growers in this region. In this study, we used morphological characteristics combined with multilocus phylogenetic analyses of the ITS , act , rpb2 , tef1 and tub2 loci to identify isolates from apple (Malus domestica) and wild apple (M. sieversii). As a result, C. hippophaopsis sp. nov. from M. sieversii and C. shawanensis sp. nov. from M. domestica were discovered and proposed herein. Pathogenicity tests were further conducted on 13 varieties of apple and wild apple, which confirmed C. hippophaopsis and C. shawanensis as canker pathogens. Meanwhile, C. hippophaopsis is generally more aggressive than C. shawanensis on the tested varieties of apple and wild apple. [ABSTRACT FROM AUTHOR]

Rapid detection of plant diseases before they escalate can improve disease control. Our team has developed rapid nucleic acid extraction methods with microneedles and combined these with loop-mediated amplification (LAMP) assays for pathogen detection in the field. In this work, we developed LAMP assays for early blight (Alternaria linariae, A. alternata, and A. solani) and bacterial spot of tomato (Xanthomonas perforans) and validated these LAMP assays and two previously developed LAMP assays for tomato spotted wilt virus and late blight. Tomato plants were inoculated, and disease severity was measured. Extractions were performed using microneedles, and LAMP assays were run in tubes (with hydroxynaphthol blue) on a heat block or on a newly designed microfluidic slide chip on a heat block or a slide heater. Fluorescence on the microfluidic chip slides was visualized using EvaGreen and photographed on a smartphone. Plants inoculated with X. perforans or tomato spotted wilt virus tested positive prior to visible disease symptoms, whereas Phytophthora infestans and A. linariae were detected at the time of visual disease symptoms. LAMP assays were more sensitive than PCR, and the limit of detection was 1 pg of DNA for both A. linariae and X. perforans. The LAMP assay designed for early blight detected all three species of Alternaria that infect tomato and is thus an Alternaria spp. assay. This study demonstrates the utility of rapid microneedle extraction followed by LAMP on a microfluidic chip for rapid diagnosis of four important tomato pathogens. [ABSTRACT FROM AUTHOR]

Plant-pathogenic fungi produce toxins as virulence factors in many plant diseases. In Cercospora leaf blight of soybean caused by Cercospora cf. flagellaris, symptoms are a consequence of the production of a perylenequinone toxin, cercosporin, which is light-activated to produce damaging reactive oxygen species. Cercosporin is universally toxic to cells, except to the cells of the producer. The current model of self-resistance to cercosporin is largely attributed to the maintenance of cercosporin in a chemically reduced state inside hyphae, unassociated with cellular organelles. However, in another perylenequinone-producing fungus, Phaeosphaeria sp., the toxin was specifically sequestered inside lipid droplets (LDs) to prevent reactive oxygen species production. This study hypothesized that LD-based sequestration of cercosporin occurred in C. cf. flagellaris and that lipid-inhibiting fungicides could inhibit toxin production. Confocal microscopy using light-cultured C. cf. flagellaris indicated that 3-day-old hyphae contained two forms of cercosporin distributed in two types of hyphae. Reduced cercosporin was uniformly distributed in the cytoplasm of thick, primary hyphae, and, contrary to previous studies, active cercosporin was observed specifically in the LDs of thin, secondary hyphae. The production of hyphae of two different thicknesses, a characteristic of hemibiotrophic plant pathogens, has not been documented in C. cf. flagellaris. No correlation was observed between cercosporin production and total lipid extracted, and two lipid-inhibiting fungicides had little effect on fungal growth in growth-inhibition assays. This study lays a foundation for exploring the importance of pathogen lifestyle, toxin production, and LD content in the pathogenicity and symptomology of Cercospora. [ABSTRACT FROM AUTHOR]

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence. microsensor networks. and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems. allowing for targeted management of crops. contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts ft·orn diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority. with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations. [ABSTRACT FROM AUTHOR]

Plant pathogens pose a high risk of yield losses and threaten food security. Technological and scientific advances have improved our understanding of the molecular processes underlying host–pathogen interactions, which paves the way for new strategies in crop disease management beyond the limits of conventional breeding. Cross‐family transfer of immune receptor genes is one such strategy that takes advantage of common plant immune signalling pathways to improve disease resistance in crops. Sensing of microbe‐ or host damage‐associated molecular patterns (MAMPs/DAMPs) by plasma membrane‐resident pattern recognition receptors (PRR) activates pattern‐triggered immunity (PTI) and restricts the spread of a broad spectrum of pathogens in the host plant. In the model plant Arabidopsis thaliana, the S‐domain receptor‐like kinase LIPOOLIGOSACCHARIDE‐SPECIFIC REDUCED ELICITATION (AtLORE, SD1‐29) functions as a PRR, which senses medium‐chain‐length 3‐hydroxylated fatty acids (mc‐3‐OH‐FAs), such as 3‐OH‐C10:0, and 3‐hydroxyalkanoates (HAAs) of microbial origin to activate PTI. In this study, we show that ectopic expression of the Brassicaceae‐specific PRR AtLORE in the solanaceous crop species Solanum lycopersicum leads to the gain of 3‐OH‐C10:0 immune sensing without altering plant development. AtLORE‐transgenic tomato shows enhanced resistance against Pseudomonas syringae pv. tomato DC3000 and Alternaria solani NL03003. Applying 3‐OH‐C10:0 to the soil before infection induces resistance against the oomycete pathogen Phytophthora infestans Pi100 and further enhances resistance to A. solani NL03003. Our study proposes a potential application of AtLORE‐transgenic crop plants and mc‐3‐OH‐FAs as resistance‐inducing biostimulants in disease management. [ABSTRACT FROM AUTHOR]

For millions of people worldwide, rice is one of the main food crops. Nevertheless, while being grown, rice is susceptible to many diseases. Most rice plant diseases are influenced by biotic and abiotic factors, including nematodes, viroids, fungus, viruses, bacteria, and other microorganisms, as well as temperature and other environmental factors. Thus, an automatic early classification of leaf disease is necessary to improve the rice yield. In this paper, for identifying and categorizing the rice leaf disease, a convolutional neural network (CNN) model is used, and the CNN is trained using the Remora Optimization Algorithm (ROA). A better classification outcome is attained by performing the segmentation process using K‐means with the Fractional Tangential‐Spherical Kernel (FTSK) algorithm. Furthermore, the developed Remora Optimization‐ Convolutional Neural Network (Remora‐CNN) method achieved the optimal performance based on the testing accuracy, sensitivity and specificity of 0.925, 0.931, and 0.941 using the Rice Leaf Disease Image Samples Dataset. [ABSTRACT FROM AUTHOR]

The current study was carried out to assess the alkaloid‐enriched constituents of Prosopis juliflora (Fabaceae) against bacterial blight of pomegranate. Utilising plant extracts for disease management offers an eco‐friendly approach. In vitro assessment of the minimum inhibitory concentration (MIC) for alkaloid‐enriched fractions from the leaves and inflorescence of P. juliflora revealed a 14 mm zone of inhibition, with the lowest MIC value being 30 mg/mL. Untargeted metabolomics analysis of alkaloid‐rich fractions (ARF) from four different tissues of P. juliflora, subjected to LC–MS for alkaloid identification, detected a total of 1489 peaks in positive mode and 240 peaks in negative mode. The highest peaks were detected in the leaves, where 47 metabolites and 9 alkaloids were identified. Ellipticine and ormosanine were found in relatively high concentrations across all four tissues, as indicated by their peak areas. The inflorescence had significantly high concentrations of sparteine, solanidine‐I and puberanidine compared with other parts. Additionally, acetylsolanine was more abundant in the inflorescence, while solasodine‐I was more prevalent in the leaves. A substantial amount of strictosidine was also found in the inflorescence. [ABSTRACT FROM AUTHOR]

African eggplant (AEP) (Solanum aethiopicum group Gilo) is an important vegetable with considerable economic value in Ghana and tropical Africa. However, fungal diseases threaten its cultivation. Surveys conducted in 2021 and 2022 growing seasons across 35 commercial farms in five regions of Ghana revealed symptoms of crown rot and wilt affecting AEP. This study was undertaken to identify and characterise 36 fungal isolates causing these diseases in AEPs using morphological, molecular and pathogenicity assays. Morphological and molecular analyses of the Btub2, Tef‐1α and Rpb2 sequences identified two Fusarium species (F. elaeidis and F. fredkrugeri) and three Neocosmospora species (N. falciforme, N. suttoniana and N. solani) associated with the plant diseases. F. elaeidis (14 isolates) and N. falciforme (14) were the most commonly isolated species from symptomatic plants. Specifically, F. elaeidis was found in wilting plants, while F. fredkrugeri and the three Neocosmospora spp. were more associated with wilting plants with crown rot symptoms than plants with only wilt symptoms. All identified species exhibited pathogenicity when inoculated onto AEP roots and stems, confirming field observations. F. elaeidis was the most aggressive in inducing wilt symptoms, while N. solani and N. suttoniana were particularly aggressive in inducing crown rot symptoms. This study is the first to document that F. elaeidis, F. fredkrugeri, N. falciforme and N. suttoniana are pathogens causing wilt and crown rot in AEP in Ghana. These findings provide essential insights for developing effective disease management strategies to reduce losses from these fungal species. [ABSTRACT FROM AUTHOR]

Persimmon (Diospyros kaki) is an important fruit crop that is widely cultivated in China, and its production is severely affected by anthracnose. Endophytic bacterial communities play an important role in the establishment of plant diseases, although little is known about these endophytes in persimmon plants. Fangshan persimmon is well known for its excellent quality in Zhejiang Province, China. The present study demonstrated that the Colletotrichum complex was the causal agent of anthracnose in persimmon in Zhejiang. High‐throughput sequencing revealed that bacterial communities in Fangshan persimmon twigs were dominated by the phyla Proteobacteria, Actinobacteriota, Firmicutes and Bacteroidota. Persimmon twigs infected with Colletotrichum showed obvious shifts in the endophytic bacterial communities, and these shifts in slight infections varied from those in severe infections. For slight infections, the genera Pseudomonas and Burkholderia–Caballeronia–Paraburkholderia were significantly more abundant in slightly symptomatic twigs than in healthy twigs, whereas the genera 1174‐901‐12, Methylocella and Streptococcus exhibited the opposite trend. In severe infections, the genera Curtobacterium, Pseudomonas and Kineococcus were significantly more abundant in severely symptomatic twigs than in healthy twigs, whereas the genera 1174‐901‐12, Lactobacillus and Amnibacterium exhibited opposite trends. The genera Pseudomonas and Curtobacterium showed the strongest positive correlation with anthracnose. This is the first report of endophytic bacterial communities associated with persimmon plants and their interaction with anthracnose disease, and these findings are important for developing biocontrol strategies for persimmon anthracnose. [ABSTRACT FROM AUTHOR]

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection's severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures. [ABSTRACT FROM AUTHOR]

The interplay between plant hosts, phytopathogenic bacteria, and enteric human pathogens in the phyllosphere has consequences for human health. Salmonella enterica has been known to take advantage of phytobacterial infection to increase its success on plants, but there is little knowledge of additional factors that may influence the relationship between enteric pathogens and plant disease. In this study, we investigated the role of humidity and the extent of plant disease progression on S. enterica colonization of plants. We found that high humidity was necessary for the replication of S. enterica on diseased lettuce, but not required for S. enterica ingress into the UV-protected apoplast. Additionally, the Xanthomonas hortorum pv. vitians (hereafter, X. vitians)-infected lettuce host was found to be a relatively hostile environment for S. enterica when it arrived prior to the development of watersoaking or following necrosis onset, supporting the existence of an ideal window during X. vitians infection progress that maximizes S. enterica survival. In vitro growth studies in sucrose media suggest that X. vitians may allow S. enterica to benefit from cross-feeding during plant infection. Overall, this study emphasizes the role of phytobacterial disease as a driver of S. enterica success in the phyllosphere, demonstrates how the time of arrival during disease progress can influence S. enterica's fate in the apoplast, and highlights the potential for humidity to transform an infected apoplast into a growth-promoting environment for bacterial colonizers. [ABSTRACT FROM AUTHOR]

Plant leaf infections are a common threat to global production in both the long and short terms, affecting not only many farmers but also consumers around the world. Early detection and treatment of plant leaf diseases are essential to promote healthy plant growth in agriculture and ensure sufficient supply and health security for the world’s geometric (population) growth. Detection of plant leaf diseases using computer-aided technologies is widespread today. In the first part of this thesis, we describe an artificial intelligence (AI) model that enables image analysis to facilitate disease detection and then present its contribution at the societal level. We used images of maize leaves and images of apples in fields from the standard PlantVillage repository for object localization. An efficient deep learning (DL) modified mask region convolutional neural network (Mask R-CNN) is proposed for autonomous segmentation and detection of the object to be analyzed in this research. The proposed work exploited the features learned by the Mask R-CNN model at various processing hierarchies. We achieved improved code generation of singular images of the detected objects and an overall accuracy of 98.89% on the validation sets. In the rest of our research, we wanted to show the impact of our solution at a social level while highlighting the important aspects that characterize good development. The specificity of this approach is to present the different AI solutions used for the analysis of agricultural crops, with the aim of highlighting their benefits and their impact on human activities. [ABSTRACT FROM AUTHOR]

Opinion leaders play a significant role in promoting innovation and technology adoption through effective information dissemination to potential adopters. However, personal traits that define effective opinion leaders, as identified in existing literature, may not fully align with followers' desired attributes of an opinion leader. Additionally, little is known about how varying degrees of influence may impact the spread of knowledge. To bridge the gap, first, we conducted focus group discussions in 10 villages within the banana farming community in Rwanda to assess the critical attributes that farmers seek in opinion leaders. Results indicate that honesty, commitment, social, knowledge, and leading by example are the top five attributes that farmers value the most from an opinion leader for the control of Banana Xanthomonas Wilt – a major banana disease. Second, we collected data from 100 nominated opinion leaders, including 10 farmer promoters, using a structured questionnaire to determine how they transmit knowledge about disease management to farmers. The opinion leaders were categorised into low, medium, and high levels of influence based on pairwise comparison. We found that honesty, leading by example, and justice are significant attributes that distinguish opinion leaders with a higher level of influence and who are more effective in transmitting knowledge. Farmer promoters are not distinct from other opinion leaders in terms of access to farmers but are more active in knowledge transmission than any other opinion leader. Our results suggest that having access to farmers does not indicate influence. Therefore, we recommend engaging those farmer promoters and opinion leaders with a higher level of influence to disseminate information to farmers at the village level. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid and accurate classification of Panax notoginseng leaf diseases is vital for timely disease control and reducing economic losses. Recently, image classification algorithms have shown great promise for plant disease diagnosis, but dataset quantity and quality are crucial. Moreover, classifying P. notoginseng leaf diseases faces severe challenges, including the small features of anthrax and the strong similarity between round spot and melasma diseases. In order to address these problems, we have proposed an ECA-based diffusion model and Inception-SSNet for the classification of the six major P. notoginseng leaf diseases, namely gray mold, powdery mildew, virus infection, anthrax, melasma, and round spot. Specifically, we propose an image generation scheme, in which the lightweight attention mechanism, ECA, is used to capture the dependencies between channels for improving the dataset quantity and quality. To extract disease features more accurately, we developed an Inception-SSNet hybrid model with skip connection, attention feature fusion, and self-calibrated convolutional. These innovative methods enable the model to make better use of local and global information, especially when dealing with diseases with similar features and small targets. The experimental results show that our proposed ECA-based diffusion model FID reaches 42.73, compared with the baseline model, which improved by 74.71%. Further, we tested the classification model using the data set of P. notoginseng leaf disease generation, and the accuracy of 11 mainstream classification models was improved. Our proposed Inception-SSNet classification model achieves an accuracy of 97.04% on the non-generated dataset, which is an improvement of 0.11% compared with the baseline model. On the generated dataset, the accuracy reached 99.44%, which is an improvement of 1.02% compared to the baseline model. This study provides an effective solution for the monitoring of Panax notoginseng diseases. [ABSTRACT FROM AUTHOR]

The high performance of deep learning networks relies on large datasets and powerful computational resources. However, collecting enough diseased training samples is a daunting challenge. In addition, existing few-shot learning models tend to suffer from large size, which makes their deployment on edge devices difficult. To address these issues, this study proposes a pruning-based lightweight few-shot learning (Prune-FSL) approach, which aims to utilize a very small number of labeled samples to identify unknown classes of crop diseases and achieve lightweighting of the model. First, the disease few-shot learning model was built through a metric-based meta-learning framework to address the problem of sample scarcity. Second, a slimming pruning method was used to trim the network channels by the γ coefficients of the BN layer to achieve efficient network compression. Finally, a meta-learning pruning strategy was designed to enhance the generalization ability of the model. The experimental results show that with 80% parameter reduction, the Prune-FSL method reduces the Macs computation from 3.52 G to 0.14 G, and the model achieved an accuracy of 77.97% and 90.70% in 5-way 1-shot and 5-way 5-shot, respectively. The performance of the pruned model was also compared with other representative lightweight models, yielding a result that outperforms those of five mainstream lightweight networks, such as Shufflenet. It also achieves 18-year model performance with one-fifth the number of parameters. In addition, this study demonstrated that pruning after sparse pre-training was superior to the strategy of pruning after meta-learning, and this advantage becomes more significant as the network parameters are reduced. In addition, the experiments also showed that the performance of the model decreases as the number of ways increases and increases as the number of shots increases. Overall, this study presents a few-shot learning method for crop disease recognition for edge devices. The method not only has a lower number of parameters and higher performance but also outperforms existing related studies. It provides a feasible technical route for future small-sample disease recognition under edge device conditions. [ABSTRACT FROM AUTHOR]

This paper discusses the mechanisms by which fungi manipulate plant physiology and suppress plant defense responses by producing effectors that can target various host proteins. Effector-triggered immunity and effector-triggered susceptibility are pivotal elements in the complex molecular dialogue underlying plant–pathogen interactions. Pathogen-produced effector molecules possess the ability to mimic pathogen-associated molecular patterns or hinder the binding of pattern recognition receptors. Effectors can directly target nucleotide-binding domain, leucine-rich repeat receptors, or manipulate downstream signaling components to suppress plant defense. Interactions between these effectors and receptor-like kinases in host plants are critical in this process. Biotrophic fungi adeptly exploit the signaling networks of key plant hormones, including salicylic acid, jasmonic acid, abscisic acid, and ethylene, to establish a compatible interaction with their plant hosts. Overall, the paper highlights the importance of understanding the complex interplay between plant defense mechanisms and fungal effectors to develop effective strategies for plant disease management. [ABSTRACT FROM AUTHOR]

Plant activators have emerged as promising alternatives to conventional crop protection chemicals for managing crop diseases due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce disease resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of pathogen resistance development. Among the various defensive pathways targeted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful development of commercial activators of systemic acquired resistance, such as benzothiadiazole, for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed, from whole-plant bioassays to molecular and transgenic approaches. We elaborate on the various components, biological significance, and regulatory circuits governing the SA pathway while critically examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials assessing the practical applicability of such activators are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent endeavors in China. Collectively, this comprehensive review aims to describe existing knowledge and provide a roadmap for future research toward developing more potent plant activators that enhance crop health. [ABSTRACT FROM AUTHOR]

Crop disease often leads to field epidemics with serious threats to yield. Early symptoms are sometimes difficult to identify, so the origin of primary inoculum is a critical focal point in the study of plant diseases, as it can help design management strategies to reduce crop losses. Here, we investigated whether anthracnose of water yams (Dioscorea alata L.) caused by the species complex Colletotrichum gloeosporioides can start from infected seed tubers from the previous harvest. Over two years, we collected tubers with varying pathogen prevalence in the field directly from producers and conducted fungal isolations in the lab to sample C. gloeosporioides. We also proceeded to artificially inoculate tubers before planting and monitored disease development. Finally, we genotyped isolates from leaves in the fields and assessed fixation indices between plots based on plot ownership (plots with a common seed tuber origin from a single farmer) vs. samples in plots from unrelated producers in Guadeloupe, Martinique, and Barbados. We were unable to isolate the fungus from harvested tubers in either sampling survey nor did any plants grown from inoculated tubers develop any disease symptoms during growth. Also, the genetic structure of samples within each plot was independent of plot ownership, though this occurred with varying levels in the different islands. These results suggest that contaminated planting material from seed tubers is not the primary source of the disease, which is in contrast to the common perception of yam anthracnose prevalence in the Antilles. [ABSTRACT FROM AUTHOR]