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Huanglongbing (HLB) is a fatal citrus disease that is currently threatening citrus varieties worldwide. One putative causative agent, Candidatus Liberibacter asiaticus (CLas), is vectored by Diaphorina citri, known as the Asian citrus psyllid (ACP). Understanding the details of CLas infection in HLB disease has been hindered by its Candidatus nature and the inability to confidently detect it in diseased trees during the asymptomatic stage. To identify early changes in citrus metabolism in response to inoculation of CLas using its natural psyllid vector, leaves from Madam Vinous sweet orange (Citrus sinensis (L.) Osbeck) trees were exposed to CLas-positive ACP or CLas-negative ACP and longitudinally analyzed using transcriptomics (RNA sequencing), proteomics (liquid chromatography-tandem mass spectrometry; data available in Dryad: 10.25338/B83H1Z), and metabolomics (proton nuclear magnetic resonance). At 4 weeks postexposure (wpe) to psyllids, the initial HLB plant response was primarily to the ACP and, to a lesser extent, the presence or absence of CLas. Additionally, analysis of 4, 8, 12, and 16 wpe identified 17 genes and one protein as consistently differentially expressed between leaves exposed to CLas-positive ACP versus CLas-negative ACP. This study informs identification of early detection molecular targets and contributes to a broader understanding of vector-transmitted plant pathogen interactions.

Saponins are plant secondary metabolites comprising glycosylated triterpenoids, steroids or steroidal alkaloids with a broad spectrum of toxicity to microbial pathogens and pest organisms that contribute to basal plant defense to biotic attack. Secretion of glycosyl hydrolases that enzymatically convert saponins into less toxic products was thus far the only mechanism reported to enable fungal pathogens to colonize their saponin-containing host plant(s). We studied the mechanisms that the fungus Botrytis cinerea utilizes to be tolerant to well-characterized, structurally related saponins from tomato and Digitalis purpurea. By gene expression studies, comparative genomics, enzyme assays and testing a large panel of fungal (knockout and complemented) mutants, we unraveled four distinct cellular mechanisms that participate in the mitigation of the toxic activity of these saponins and in virulence on saponin-producing host plants. The enzymatic deglycosylation that we identified is novel and unique to this fungus-saponin combination. The other three tolerance mechanisms operate in the fungal membrane and are mediated by protein families that are widely distributed in the fungal kingdom. We present a spatial and temporal model on how these mechanisms jointly confer tolerance to saponins and discuss the repercussions of these findings for other plant pathogenic fungi, as well as human pathogens.

BACKGROUND: Sclerotinia spp. are generalist fungal pathogens, infecting over 700 plant hosts worldwide, including major crops. While host resistance is the most sustainable and cost-effective method for disease management, complete resistance to Sclerotinia diseases is rare. We recently identified soft basal stem as a potential susceptibility factor to Sclerotinia minor infection in lettuce (Lactuca sativa) under greenhouse conditions. RESULTS: Analysis of stem and root cell wall composition in five L. sativa and one L. serriola accessions with varying growth habits and S. minor resistance levels revealed strong association between hemicellulose constituents, lignin polymers, disease phenotypes, and basal stem mechanical strength. Accessions resistant to basal stem degradation consistently exhibited higher levels of syringyl, guaiacyl, and xylose, but lower levels of fucose in stems. These findings suggest that stem cell wall polymers recalcitrant to breakdown by lignocellulolytic enzymes may contribute to stem strength-mediated resistance against S. minor. CONCLUSIONS: The lignin content, particularly guaiacyl and syringyl, along with xylose could potentially serve as biomarkers for identifying more resistant lettuce accessions and breeding lines. Basal stem degradation by S. minor was influenced by localized microenvironment conditions around the stem base of the plants.

BackgroundSoybean cyst nematodes (SCN) as animal parasites of plants are not usually interested in killing the host but are rather focused on completing their life cycle to increase population, resulting in substantial yield losses. Remarkably, some agricultural soils after long-term crop monoculture show a significant decline in SCN densities and suppress disease in a sustainable and viable manner. However, relatively little is known about the microbes and mechanisms operating against SCN in such disease-suppressive soils.ResultsGreenhouse experiments showed that suppressive soils (S) collected from two provinces of China and transplantation soils (CS, created by mixing 10% S with 90% conducive soils) suppressed SCN. However, SCN suppressiveness was partially lost or completely abolished when S soils were treated with heat (80 °C) and formalin. Bacterial community analysis revealed that the specific suppression in S and CS was mainly associated with the bacterial phylum Bacteroidetes, specifically due to the enrichment of Chitinophaga spp. and Dyadobacter sp., in the cysts. SCN cysts colonized by Chitinophaga spp. showed dramatically reduced egg hatching, with unrecognizable internal body organization of juveniles inside the eggshell due to chitinase activity. Whereas, Dyadobacter sp. cells attached to the surface coat of J2s increased soybean resistance against SCN by triggering the expression of defence-associated genes. The disease-suppressive potential of these bacteria was validated by inoculating them into conducive soil. The Dyadobacter strain alone or in combination with Chitinophaga strains significantly decreased egg densities after one growing cycle of soybeans. In contrast, Chitinophaga strains alone required more than one growing cycle to significantly reduce SCN egg hatching and population density.ConclusionThis study revealed how soybean monoculture for decades induced microbiota homeostasis, leading to the formation of SCN-suppressive soil. The high relative abundance of antagonistic bacteria in the cyst suppressed the SCN population both directly and indirectly. Because uncontrolled proliferation will likely lead to quick demise due to host population collapse, obligate parasites like SCN may have evolved to modulate virulence/proliferation to balance these conflicting needs. Video Abstract.

The unculturable bacterium 'Candidatus Liberibacter solanacearum' (CLso) is responsible for a growing number of emerging crop diseases. However, we know little about the diversity and ecology of CLso and its psyllid vectors outside of agricultural systems, which limits our ability to manage crop disease and understand the impacts this pathogen may have on wild plants in natural ecosystems. In North America, CLso is transmitted to crops by the native potato psyllid (Bactericera cockerelli). However, the geographic and host plant range of the potato psyllid and CLso beyond the borders of agriculture are not well understood. A recent study of historic herbarium specimens revealed that a unique haplotype of CLso was present infecting populations of the native perennial Solanum umbelliferum in California decades before CLso was first detected in crops. We hypothesized that this haplotype and other potentially novel CLso variants are still present in S. umbelliferum populations. To test this, we surveyed populations of S. umbelliferum in Southern California for CLso and potato psyllid vectors. We found multiple haplotypes of CLso and the potato psyllid associated with these populations, with none of these genetic variants having been previously reported in California crops. These results suggest that CLso and its psyllid vectors are much more widespread and diverse in North American natural plant communities than suggested by data collected solely from crops and weeds in agricultural fields. Further characterization of these apparently asymptomatic haplotypes will facilitate comparison with disease-causing variants and provide insights into the continued emergence and spread of CLso.

Plant pathogenic bacteria often have a narrow host range, which can vary among different isolates within a population. Here, we investigated the host range of the tomato pathogen Clavibacter michiganensis (Cm). We determined the genome sequences of 40 tomato Cm isolates and screened them for pathogenicity on tomato and eggplant. Our screen revealed that out of the tested isolates, five were unable to cause disease on any of the hosts, 33 were exclusively pathogenic on tomato, and two were capable of infecting both tomato and eggplant. Through comparative genomic analyses, we identified that the five non-pathogenic isolates lacked the chp/tomA pathogenicity island, which has previously been associated with virulence in tomato. In addition, we found that the two eggplant-pathogenic isolates encode a unique allelic variant of the putative serine hydrolase chpG (chpGC), an effector that is recognized in eggplant. Introduction of chpGC into a chpG inactivation mutant in the eggplant-non-pathogenic strain Cm101, failed to complement the mutant, which retained its ability to cause disease in eggplant and failed to elicit hypersensitive response (HR). Conversely, introduction of the chpG variant from Cm101 into an eggplant pathogenic Cm isolate (C48), eliminated its pathogenicity on eggplant, and enabled C48 to elicit HR. Our study demonstrates that allelic variation in the chpG effector gene is a key determinant of host range plasticity within Cm populations.

Sorghum bicolor is among the most important cereals globally and a staple crop for smallholder farmers in sub-Saharan Africa. Approximately 20% of sorghum yield is lost annually in Africa due to infestation with the root parasitic weed Striga hermonthica. Existing Striga management strategies are not singularly effective and integrated approaches are needed. Here, we demonstrate the functional potential of the soil microbiome to suppress Striga infection in sorghum. We associate this suppression with microbiome-mediated induction of root endodermal suberization and aerenchyma formation and with depletion of haustorium-inducing factors, compounds required for the initial stages of Striga infection. We further identify specific bacterial taxa that trigger the observed Striga-suppressive traits. Collectively, our study describes the importance of the soil microbiome in the early stages of root infection by Striga and pinpoints mechanisms of Striga suppression. These findings open avenues to broaden the effectiveness of integrated Striga management practices.

Fungi use the accessory gene content of their pangenomes to adapt to their environments. While gene presence-absence variation contributes to shaping accessory gene reservoirs, the genomic contexts that shape these events remain unclear. Since pangenome studies are typically species-wide and do not analyze different populations separately, it is yet to be uncovered whether presence-absence variation patterns and mechanisms are consistent across populations. Fungal plant pathogens are useful models for studying presence-absence variation because they rely on it to adapt to their hosts, and members of a species often infect distinct hosts. We analyzed gene presence-absence variation in the blast fungus, Magnaporthe oryzae (syn. Pyricularia oryzae), and found that presence-absence variation genes involved in host-pathogen and microbe-microbe interactions may drive the adaptation of the fungus to its environment. We then analyzed genomic and epigenomic features of presence-absence variation and observed that proximity to transposable elements, gene GC content, gene length, expression level in the host, and histone H3K27me3 marks were different between presence-absence variation genes and conserved genes. We used these features to construct a model that was able to predict whether a gene is likely to experience presence-absence variation with high precision (86.06%) and recall (92.88%) in M. oryzae. Finally, we found that presence-absence variation genes in the rice and wheat pathotypes of M. oryzae differed in their number and their genomic context. Our results suggest that genomic and epigenomic features of gene presence-absence variation can be used to better understand and predict fungal pangenome evolution. We also show that substantial intra-species variation can exist in these features.

The Pseudomonas syringae species complex is a heterogeneous group of plant pathogenic bacteria associated with a wide distribution of plant species. Advances in genomics are revealing the complex evolutionary history of this species complex and the wide array of genetic adaptations underpinning their diverse lifestyles. Here, we genomically characterize two P. syringae isolates collected from diseased Callery pears (Pyrus calleryana) in Berkeley, California in 2019 and 2022. We also isolated a lytic bacteriophage, which we characterized and evaluated for biocontrol efficiency. Using a multilocus sequence analysis and core genome alignment, we classified the P. syringae isolates as members of phylogroup 2, related to other strains previously isolated from Pyrus and Prunus. An analysis of effector proteins demonstrated an evolutionary conservation of effectoromes across isolates classified in PG2 and yet uncovered unique effector profiles for each, including the two newly identified isolates. Whole-genome sequencing of the associated phage uncovered a novel phage genus related to Pseudomonas syringae pv. actinidiae phage PHB09 and the Flaumdravirus genus. Finally, using in planta infection assays, we demonstrate that the phage was equally useful in symptom mitigation of immature pear fruit regardless of the Pss strain tested. Overall, this study demonstrates the diversity of P. syringae and their viruses associated with ornamental pear trees, posing spill-over risks to commercial pear trees and the possibility of using phages as biocontrol agents to reduce the impact of disease.IMPORTANCEGlobal change exacerbates the spread and impact of pathogens, especially in agricultural settings. There is a clear need to better monitor the spread and diversity of plant pathogens, including in potential spillover hosts, and for the development of novel and sustainable control strategies. In this study, we characterize the first described strains of Pseudomonas syringae pv. syringae isolated from Callery pear in Berkeley, California from diseased tissues in an urban environment. We show that these strains have divergent virulence profiles from previously described strains and that they can cause disease in commercial pears. Additionally, we describe a novel bacteriophage that is associated with these strains and explore its potential to act as a biocontrol agent. Together, the data presented here demonstrate that ornamental pear trees harbor novel P. syringae pv. syringae isolates that potentially pose a risk to local fruit production, or vice versa-but also provide us with novel associated phages, effective in disease mitigation.

Autophagy in eukaryotes functions to maintain homeostasis by degradation and recycling of long-lived and unwanted cellular materials. Autophagy plays important roles in pathogenicity of various fungal pathogens, suggesting that autophagy is a novel target for development of antifungal compounds. Here, we describe bioluminescence resonance energy transfer (BRET)-based high-throughput screening (HTS) strategy to identify compounds that inhibit fungal ATG4 cysteine protease-mediated cleavage of ATG8 that is critical for autophagosome formation. We identified ebselen (EB) and its analogs ebselen oxide (EO) and 2-(4-methylphenyl)-1,2-benzisothiazol-3(2H)-one (PT) as inhibitors of fungal pathogens Botrytis cinerea and Magnaporthe oryzae ATG4-mediated ATG8 processing. The EB and its analogs inhibit spore germination, hyphal development, and appressorium formation in Ascomycota pathogens, B. cinerea, M. oryzae, Sclerotinia sclerotiorum and Monilinia fructicola. Treatment with EB and its analogs significantly reduced fungal pathogenicity. Our findings provide molecular insights to develop the next generation of antifungal compounds by targeting autophagy in important fungal pathogens.

BACKGROUND: Fungal plant pathogens have dynamic genomes that allow them to rapidly adapt to adverse conditions and overcome host resistance. One way by which this dynamic genome plasticity is expressed is through effector gene loss, which enables plant pathogens to overcome recognition by cognate resistance genes in the host. However, the exact nature of these loses remains elusive in many fungi. This includes the tomato pathogen Cladosporium fulvum, which is the first fungal plant pathogen from which avirulence (Avr) genes were ever cloned and in which loss of Avr genes is often reported as a means of overcoming recognition by cognate tomato Cf resistance genes. A recent near-complete reference genome assembly of C. fulvum isolate Race 5 revealed a compartmentalized genome architecture and the presence of an accessory chromosome, thereby creating a basis for studying genome plasticity in fungal plant pathogens and its impact on avirulence genes. RESULTS: Here, we obtained near-complete genome assemblies of four additional C. fulvum isolates. The genome assemblies had similar sizes (66.96 to 67.78 Mb), number of predicted genes (14,895 to 14,981), and estimated completeness (98.8 to 98.9%). Comparative analysis that included the genome of isolate Race 5 revealed high levels of synteny and colinearity, which extended to the density and distribution of repetitive elements and of repeat-induced point (RIP) mutations across homologous chromosomes. Nonetheless, structural variations, likely mediated by transposable elements and effecting the deletion of the avirulence genes Avr4E, Avr5, and Avr9, were also identified. The isolates further shared a core set of 13 chromosomes, but two accessory chromosomes were identified as well. Accessory chromosomes were significantly smaller in size, and one carried pseudogenized copies of two effector genes. Whole-genome alignments further revealed genomic islands of near-zero nucleotide diversity interspersed with islands of high nucleotide diversity that co-localized with repeat-rich regions. These regions were likely generated by RIP, which generally asymmetrically affected the genome of C. fulvum. CONCLUSIONS: Our results reveal new evolutionary aspects of the C. fulvum genome and provide new insights on the importance of genomic structural variations in overcoming host resistance in fungal plant pathogens.

The Asian Citrus Psyllid (ACP), Diaphorina citri, is a vector of the pathological bacterium Candidatus Liberibacter asiaticus (CLas), which causes the most devastating disease to the citrus industry worldwide, known as greening or huanglongbing (HLB). Earlier field tests with an acetic acid-based lure in greening-free, Valencia citrus orange groves in California showed promising results. The same type of lures tested in São Paulo, Brazil, showed unsettling results. During the unsuccessful trials, we noticed a relatively large proportion of females in the field, ultimately leading us to test field-collected males and females for Wolbachia and CLas. The results showed high rates of Wolbachia and CLas infection in field populations. We then compared the olfactory responses of laboratory-raised, CLas-free, and CLas-infected males to acetic acid. As previously reported, CLas-uninfected males responded to acetic acid at 1 µg. Surprisingly, CLas-infected males required 50 × higher doses of the putative sex pheromone, thus explaining the failure to capture CLas-infected males in the field. CLas infection was also manifested in electrophysiological responses. Electroantennogram responses from CLas-infected ACP males were significantly higher than those obtained with uninfected males. To the best of our knowledge, this is the first report of a pathogen infection affecting a vectors response to a sex attractant.

Tomato spotted wilt orthotospovirus (TSWV) causes substantial economic loss to tomato production, and the Sw-5b resistance gene is widely deployed for management. Here, we show (i) the emergence of resistance-breaking (RB) TSWV strains in processing and fresh market tomato production in California over the past ten years, and (ii) evolutionary relationships with RB strains from other areas. A specific RT-PCR test was used to show the C118Y RB strain that emerged in Fresno County in 2016 quickly became predominant in the central production area and remained so through this study. In 2021, the C118Y strain was detected in the Northern production area, and was predominant in 2022. However, in 2023, the C118Y strain was unexpectedly detected in fewer spotted wilt samples from resistant varieties. This was due to emergence of the T120N RB strain, previously known to occur in Spain. A specific RT-PCR test was developed and used to show that the T120N RB strain was predominant in Colusa and Sutter counties (detected in 75-80% of samples), and detected in ~50% of samples from Yolo County. Pathogenicity tests confirmed California isolates of the T120N strain infected Sw-5b tomato varieties and induced severe symptoms. Another RB strain, C118F, was associated with spotted wilt samples of Sw-5 varieties from fresh market tomato production in southern California. Phylogenetic analyses with complete NSm sequences revealed that the C118Y and T120N RB strains infecting resistant processing tomato in California emerged locally, whereas those from fresh market production were more closely related to isolates from Mexico. Thus, widespread deployment of this single dominant resistance gene in California has driven the local emergence of multiple RB strains in different tomato production areas and types. These results further emphasize the need for ongoing monitoring for RB strains, and identification of sources of resistance to these strains.

The rice receptor kinase XA21 confers broad-spectrum resistance to Xanthomonas oryzae pv. oryzae (Xoo), the causal agent of rice bacterial blight disease. To investigate the relationship between the expression level of XA21 and resulting resistance, we generated independent HA-XA21 transgenic rice lines accumulating the XA21 immune receptor fused with an HA epitope tag. Whole-genome sequence analysis identified the T-DNA insertion sites in sixteen independent T0 events. Through quantification of the HA-XA21 protein and assessment of the resistance to Xoo strain PXO99 in six independent transgenic lines, we observed that XA21-mediated resistance is dose dependent. In contrast, based on the four agronomic traits quantified in these experiments, yield is unlikely to be affected by the expression level of HA-XA21. These findings extend our knowledge of XA21-mediated defense and contribute to the growing number of well-defined genomic landing pads in the rice genome that can be targeted for gene insertion without compromising yield.

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]

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]

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]

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]

The weight initialization technique for transfer learning refers to the practice of using pretrained models that can be modified to solve new problems, instead of starting the training process from scratch. In this study, six different transfer learning weight initialization strategies were proposed for plant disease detection: scratch (i.e., random initialization), pretrained model on cross‐domain (ImageNet), model trained on related domain (ISIC 2019), model trained on related domain (ISIC 2019) with cross‐domain (ImageNet) weights, model trained on same domain (PlantVillage), and model trained on same domain (PlantVillage) with cross‐domain weights (ImageNet). Weights from each strategy were transferred to a target dataset (Plant Pathology 2021). These strategies were implemented using eight deep learning architectures. It was observed that transferring from any strategy led to an average acceleration of convergence ranging from 33.88% to 73.16% in mean loss and an improvement of 8.72%–42.12% in mean F1‐score compared to the scratch strategy. Moreover, although smaller and less comprehensive than ImageNet, transferring information from the same domain or related domain proved to be competitive compared to transferring from ImageNet. This indicates that ImageNet, which is widely favoured in the literature, may not necessarily represent the most optimal transfer source for the given context. In addition, to identify which strategies have significant differences, a post hoc analysis using Tukey's HSD test was conducted. Finally, the classifications made by the proposed models were visualized using Grad‐CAM to provide a qualitative understanding of how different weight initialization strategies affect the focus areas of the models. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSIONS Coffee (Coffea arabica L.) is one of the most important commodities today, with a high economic value worldwide. Coffee leaf rust (Hemileia vastatrix Berk. et Br.) has been showing a high impact on Brazilian coffee trees among the various diseases that attack coffee. The climate has a great influence on the development of diseases, especially when fungi are the causal agents. This study aimed to carry out the zoning of climate favorability for coffee leaf rust in the traditional and main coffee‐producing regions of Brazil. The study was conducted in 13 locations in the states of Paraná, São Paulo, Rio de Janeiro, Espírito Santo, Minas Gerais, Goiás and Bahia. Air temperature and daily precipitation data for the current scenario were collected using the WorldClim version 2.1 platform for the last climatological normal and future climate change data. The ideal climate conditions for coffee leaf rust consist of a mean air temperature ranging from 21 to 25 °C and precipitation >30 mm per month. The Coupled Model Intercomparison Projects scenarios associated with the Intergovernmental Panel on Climate Change reports consisted of the Shared Socio‐Economic Pathways SSP‐1 2.6, SSP‐2 4.5, SSP‐3 7.0 and SSP‐5 8.5, the latter being considered one of the most catastrophic. All steps to carry out the suitability zoning were performed in a tool using the QGIS geographic information system software.Zoning for coffee leaf rust had three classes: favorable, relatively favorable and unfavorable. Currently, the largest coffee‐producing region in Brazil has 49.1% of its analyzed area classified as favorable, 39.2% as relatively favorable and 11.7% as unfavorable. In the current scenario, Patrocínio and Três Pontas are locations with high coffee production in which the favorable class is predominant. The state of Minas Gerais has an annual mean of 55.3% of its entire territory apt for the disease, with the highest occurrence between September and March.Climate change has a negative impact on the development of coffee leaf rust, mainly in the long term, as in the period of 2081–2100, in which the SSP‐5 8.5 scenario led to a decrease in the favorable and unfavorable areas and an increase in the relatively favorable areas of 9.8%, 18.6% and 71.5% for the Brazilian territory, respectively. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The impact of climate change on plant growth and productivity is profound, resulting in significant stresses such as biotic and abiotic factors. Among the abiotic stresses, temperature extremes, nutrient imbalances, heavy metals, drought, and salinity stand out as prominent contributors to substantial crop yield losses globally, exacerbating food insecurity concerns. Various methods are currently being explored to enhance plants' resilience to these stresses. We undertook an extensive literature review to assess the current landscape of knowledge in Nano biotechnology and identify areas where existing research is deficient or underexplored. This examination focused on identifying gaps related to methodology, scope, applications, and specific challenges within nanobiotechnology. We also evaluated recent developments and emerging trends in the field, identifying areas where research interest is expanding but remains relatively uncharted. By narrowing our research scope, we aimed to pinpoint specific aspects of Nano biotechnology requiring further investigation or that have not been sufficiently addressed. One promising approach involves the use of nanoparticles, which have shown to positively influence plant performance under stressful conditions. Nanoparticles facilitate nutrient uptake by plants, combat plant diseases and pathogens, and enable the detection and monitoring of trace components in soil through signal absorption. Understanding the mechanisms by which nanoparticles assist plants in mitigating abiotic challenges holds great promise for developing enduring solutions. However, urgent action is needed to implement rapid strategies that alleviate abiotic stress and enhance agricultural productivity. [ABSTRACT FROM AUTHOR]

The effectiveness of SiO 2 nanoparticles for their potential to control bacterial wilt (BW) disease in tomato plants was studied. Different concentrations of nanoparticles were first evaluated for their in-vitro antibacterial potential against Ralstonia solanacearum , the causal agent of BW disease. The disease suppressive effect of NP was investigated through in-planta experiments in tomato plants. Nanoparticles exhibited maximum toxicity to R. solanacearum at the concentration of 25 µg/ml and showed a 14.2 ± 0.5 mm growth inhibition zone in a lab test. Tomato plants were grown in pathogen-inoculated soil that was amended with NP suspension. The efficacy of NP application was evaluated for their effect on disease suppression, soil pathogen population, and plant growth and defense response. Results showed that NP at 150 ppm significantly reduced the pathogen population in the soil, as indicated by population count through molecular and dilution count analysis. The NP treatment suppresses the disease index and improves plant growth. The activities of plant antioxidant enzymes were increased, and the expression of resistance genes PR1, PR5, PAL , and PDF - 1.2 were enhanced after NP application. Plants grown in NP-treated soil showed improved photosynthesis and reduced oxidative stress as compared to untreated plants under pathogen stress, and the total phenolic and flavonoid content was enhanced significantly. Moreover, DPPH and FRAP content and AsA/DHA and GSH/GSSG ratios were enhanced, followed by NP application, implying that the non-enzymatic antioxidant capacity of the plants was improved. This study reveals the molecular and physiological action mechanism of managing BW in tomato plants. It suggests that SiO 2 nanoparticles could be included in the integrated management of BW disease as one of the effective components. [Display omitted] • SiO 2 nanoparticles exhibited antibacterial activity against ralstonia solanacearum. • Soil treatment with SiO 2 NP suppress pathogen population in soil. • Application of SiO 2 NP enhanced plant growth and resistance. [ABSTRACT FROM AUTHOR]

Potassium (K) fertilisation has frequently been shown to enhance plant resistance against pathogens, though the mechanisms remain elusive. This study investigates the interaction dynamics between Nicotiana benthamiana and the pathogen Alternaria longipes under different planta K levels. On the host side, adding K activated the expressions of three NLR (nucleotide‐binding domain and leucine‐rich repeat‐containing proteins) resistance genes, including NbRPM1, NbR1B23 and NbNBS12. Silencing these NLRs attenuated resistance in high‐K (HK, 40.8 g/kg) plant, whereas their overexpression strengthened resistance in low‐K (LK, 23.9 g/kg) plant. Typically, these NLRs mainly strengthened plant resistance via promoting the expression of pathogenesis‐related genes (PRs), ROS burst and synthesis of antifungal metabolites in HK plant. On the pathogen side, the expression of effectors HKCSP1, HKCSP2 and LKCSP were shown to be related to planta K content. A. longipes mainly expressed effectors HKCSP1 and HKCSP2 in HK plant to interfere host resistance. HKCSP1 physically interacted with NbRPM1 to promote the degradation of NbRPM1, then attenuated related resistance in HK N. benthamiana. Meanwhile, HKCSP2 directly interacted with NbPR5 to suppress resistance in HK plant. In LK plant, A. longipes mainly deployed LKCSP that interacted with NbR1B23 to interfere reduce resistance in N. benthamiana. Overall, our research insights that both pathogen and host mobilise distinct strategies to outcompete each other during interactions in different K nutrient environments. Summary statement: The present study demonstrated that changes in planta K content could mediate the interaction pattern between host plants and pathogens, eventually leading different outcome of plant disease. Although the pathogen tries to suppress host immune by deploying different virulence factors, the host can benefit much more due to its sufficient resistance events under high K condition. Our results add to evidence that K fertilisation can be adjusted to optimise suppression of fungal diseases and thus has value as a disease management tool. [ABSTRACT FROM AUTHOR]

Crop diseases are among the major natural disasters in agricultural production that seriously restrict the growth and development of crops, threatening food security. Timely classification, accurate identification, and the application of methods suitable for the situation can effectively prevent and control crop diseases, improving the quality of agricultural products. Considering the huge variety of crops, diseases, and differences in the characteristics of diseases during each stage, the current convolutional neural network models based on deep learning need to meet the higher requirement of classifying crop diseases accurately. It is necessary to introduce a new architecture scheme to improve the recognition effect. Therefore, in this study, we optimized the deep learning-based classification model for multiple crop leaf diseases using combined transfer learning and the attention mechanism, the modified model was deployed in the smartphone for testing. Dataset that containing 10 types of crops, 61 types of diseases, and different degrees was established, the algorithm structure based on ResNet50 was designed using transfer learning and the SE attention mechanism. The classification performances of different improvement methods were compared by model training. Result indicates that the average accuracy of the proposed TL-SE-ResNet50 model is increased by 7.7%, reaching 96.32%. The model was also integrated and implemented in the smartphone and the test result of the application reaches 94.8%, and the average response time is 882 ms. The improved model proposed has a good effect on the identification of diseases and their condition of multiple crops, and the application can meet the portable usage needs of farmers. This study can provide reference for more crop disease management research in agricultural production. [ABSTRACT FROM AUTHOR]

Ever since the first crops were domesticated over 10,000 years ago, pests and pathogens have wreaked havoc on crop production. Despite diverse threats from longstanding and emerging diseases, plants continue to play a central role in meeting humanity's requirements for calories and nutrients. In this review, we discuss historical advances made by plant pathologists and breeders that have helped keep many plant diseases at bay. We focus predominantly on fungal diseases of five food crops—rice, wheat, maize, soybean and potato—that provide the bulk of calories consumed globally. We cover such historical triumphs in plant pathology as (i) advanced knowledge of fungal biology revolutionizing disease management strategies, (ii) integration of genetics by the 'wheat wizard' Rowland Biffen and others transforming the landscape of resistance breeding and (iii) the advent of modern fungicides—from a sinking ship in the 1660s—leading to dramatic yield enhancements. We also discuss (iv) the potential for emerging biocontrol agents to holistically promote plant health and yield. Today, the high homogeneity of crops in modern agricultural systems exacerbates yield losses due to disease by increasing pest/pathogen transmission rates and eroding natural spatial barriers to pest/pathogen dispersal. Meanwhile, climate change is altering the compositions of global pest and pathogen populations and expanding pest/pathogen territories with unpredictable consequences. As we look to the future, the strength of the plant pathology community remains central to developing innovative solutions to tackle these complex and expanding threats to plant health and secure a plentiful food supply. [ABSTRACT FROM AUTHOR]

Bud rot (BR), caused by Phytophthora palmivora, limits growth and development of oil palms in the American continent, particularly in Colombia. Due to the absence of systematic epidemiological analyses and determination of risk factors associated with BR, this study aimed to employ deterministic mathematical models and frequentist and Bayesian statistical methods to quantify the genetic response and edaphoclimatic variables as risk indicators of BR. From 2011 to 2014, the severity of BR in oil palm crops was recorded monthly in two locations: Tumaco (hot spot) and Villavicencio (cold spot), determining the edaphoclimatic variables at each site. Using the area under the disease progress stairs curve, temporal models were applied to determine the rate of disease development (R0) to quantify risk at locality and genotype levels. Subsequently, the observed intensity and severity levels of BR were adjusted to models such as survival curves, Cox proportional hazard risk and transition probabilities, or Markov states, with the aim of quantifying and characterizing risk factors associated with genotype and edaphoclimatic variables at each location. The risk in Tumaco was three times higher than in Villavicencio, and the R0 values were differential by genotype, with Tenera more susceptible than Hybrids. Moreover, the risk of BR increased when periods of 2–3 months occurred with successive instances of precipitation and relative humidity greater than 150 mm/month and 90%, respectively, and when manganese and zinc levels were below optimal. This approach allowed us to characterize epidemiological factors that cause plant diseases, allowing quantification of the risk of BR. [ABSTRACT FROM AUTHOR]

Plant disease control faces a lot of challenges due to its overdependence on chemicals that have strict restrictions and regulatory requirements. With the increase in drug‐resistant pathogens and continual crop losses due to disease outbreaks, much attention has been brought to a new set of emerging antibiotics called antimicrobial peptides (AMPs). AMPs are a group of multifunctional, short‐sequence peptides that are usually cationic in nature and found in all living organisms. They are part of the innate immune system of different organisms and exhibit a wide range of inhibitory effects on microorganisms, making them potential therapeutic factors efficacious as an alternate resource for plant disease management. AMPs interact with the anionic cell membrane of the pathogen and cause cell lysis or inhibit crucial intracellular targets. AMPs can be isolated from almost all life forms ranging from microbes to mammals. In addition to these sources, AMPs are also being synthesized using recombinant methods with the goal of overcoming the constraints of natural AMPs with regard to stability, activity and toxicity. Recent advancements have been made to develop transgenic plants expressing AMPs that has proved to perform better than the use of antibiotics. This review highlights the different kinds of AMPs produced by plants and microorganisms along with their mode of action, target pathogens, structural characteristics and advancements in this field, which include isolation methods, synthetic AMPs and transgenic plants expressing AMPs. [ABSTRACT FROM AUTHOR]

Zinc oxide nanoparticles (ZnO NPs) are inorganic compounds listed as "generally recognized as safe" (GRAS) materials and have been used in plant production as well as for plant disease control. This study investigated the antibacterial efficacy of ZnO NPs with various surface areas against Xanthomonas campestris pv. campestris, assessed the effectiveness of ZnO NPs in controlling black rot disease in Chinese kale, and examined the influence of ZnO NPs application on soil bacterial communities. The results showed that ZnO NPs with high surface area effectively inhibited X. campestris pv. campestris by restraining growth and causing cell damage. Seed treatment and foliar spray application of high surface area ZnO NPs at 250 μg/mL significantly reduced the disease severity of black rot. Furthermore, in the greenhouse experiment, the soil bacterial communities in the treatment of plants applied with ZnO NPs did not differ from those in soil of nontreated plants. Therefore, ZnO NPs have the potential to serve as an alternative substance for plant disease management. [ABSTRACT FROM AUTHOR]

The walnut industry in Argentina has grown in recent years, especially in northern Patagonia. However, this expansion has also brought new challenges, including the emergence of diseases threatening the health and productivity of walnut plantations. One of the most important diseases affecting walnut crops worldwide is 'ink disease' or 'walnut dieback'. The aim of this study was to characterize ink disease, identify the aetiological agents, and estimate its incidence in walnut orchards of northern Patagonia, Argentina. Symptomatic trees were observed in the seven surveyed walnut orchards, showing a range of symptoms, including chlorosis and wilting, stem cankers and desiccation. In vivo assay and molecular analyses revealed that not only Phytophthora (P.) but also Phytopythium (Ph.) species are the causal agents of the disease, with P. drechsleri, Ph. mercuriale and Ph. vexans being the most virulent over the commonly used rootstock Juglans hindsii. Notably, Ph. vexans was the most frequently isolated pathogen, suggesting its prevalence and potential impact on the regional walnut industry. Overall, this study provides critical insights into the prevalence and impact of Phytophthora and Phytopythium species on walnut health in northern Patagonia. The presence of multiple pathogenic oomycete species raises concerns about the health of walnut orchards, as well as the potential risk of pathogen transmission to other fruit crops in the region. The findings will help growers to develop better cultural practices and implement measures to guarantee the sustainability of the walnut industry in Patagonia. [ABSTRACT FROM AUTHOR]

Tebuconazole (TEB) is a common triazole sterol demethylation inhibitor fungicide utilized to manage a variety of diseases in crops like cereals, fruits, and vegetables. The aim of this work was to assess the effects of TEB on the structure of the cerebellum in adult albino rats and possible protective impact of co-administration of Gallic acid (GA). Four groups of forty adult male albino rats were randomly selected, and the rats in group I received corn oil through daily gavage for 4 weeks. Group II received GA dissolved in the normal saline at a dose of 100 mg/kg through daily gavage for 4 weeks, group III administered with TEB dissolved in corn oil at its acceptable daily intake dose (0.02 mg/kg body weight) through daily gavage for 4 weeks, group IV rats received both TEB and GA. For light microscopic, ultrastructural, and immunohistochemical investigations, cerebellar specimens were prepared. TEB exposure led to neuronal damage in the form of degenerated Purkinje cells with vacuolated cytoplasm, areas of lost Purkinje cells, the basket cells appeared vacuolated with degenerated neuropil, the granule cells clumped with congested areas between them, dilated cerebellar islands, weak positive bcl2 immunoreactions in the Purkinje cells, and numerous GFAP-positive astrocytes. GA mitigated TEB-mediated histological changes in the cerebellar cortex. We concluded that TEB caused Purkinje neurons in the rat cerebellar cortex to degenerate and undergo apoptosis. GA had a neuroprotective benefit against TEB toxicity in the rat cerebellar cortex. [ABSTRACT FROM AUTHOR]

The growing population increases the demand for agricultural production. Owing to some plant diseases, this production is continuously decreasing. Detecting and recognising these diseases at the initial stage is crucial for the sake of high yields from crops and plants. The conventional methods involve manual detection, which can be error-prone and laborious. Automatic recognition paves a way for the regular monitoring of huge areas of crops in less time. The deep learning-based approaches have entered in a big way in the detection of diseases in plants and crops because of their ability to overcome the limitation of regular manual monitoring. The use of colour space models helps demarcate the affected area ofimages of plants and crops prior to inputting them to the CNN model so as to improve the detection accuracy. For this, different colour space models that include RGB, HSL, HSV, LAB, LUV, XYZ and YUV are investigated. However, HSL-CNN joint model performs the best by achieving 98.97% accuracy, 98.61% precision, and 99.28% F1-score. [ABSTRACT FROM AUTHOR]

The existence of crop disease leads to a substantial loss in quantity and quality in agricultural production. The regular monitoring of plants helps in preventing leaf disease at the early stages and is one of the beneficial acts in preserving the plant. This research focuses on developing an automated approach to classify plant leaf disease using Efficient CapsNet with Self-Attention Routing (EC-SAR). The raw data is obtained from plant village and CGIAR dataset which is pre-processed by color enhancement and data augmentation. Next to pre-processing, segmentation is performed using K-means clustering and Otsu threshold techniques which effectively segment the partitions of leaves. The feature extraction is performed using Gray Level Co-Occurrence Matrix (GLCM), Local Ternary Pattern (LTP) and Histogram Oriented Gradients (HoG) techniques. After this, the classification is performed using the proposed EC-SAR which is comprised of an efficient capsule network that adapts itself to the large input data. The self-attention layer routing is performed to choose a better capsule that is suitable to generate feature representation based on actual data. The experimental outcome shows that the suggested framework achieved an accuracy of 99.98% for the plant village dataset which is comparatively higher than the existing few-shot learning approach, Convolutional Block Attention Module and Efficient Channel Attention (CBAM-ECA) and Loss Fused Convolutional Neural Network (LFCNN) with accuracies of 93.45%, 99.74% and 98.93% respectively. [ABSTRACT FROM AUTHOR]

A pathogen arriving on a host typically encounters a diverse community of microbes that can shape priority effects, other within-host interactions and infection outcomes. In plants, environmental nutrients can drive trade-offs between host growth and defence and can mediate interactions between co-infecting pathogens. Nutrients may thus alter the outcome of pathogen priority effects for the host, but this possibility has received little experimental investigation. To disentangle the relationship between nutrient availability and co-infection dynamics, we factorially manipulated the nutrient availability and order of arrival of two foliar fungal pathogens (Rhizoctonia solani and Colletotrichum cereale) on the grass tall fescue (Lolium arundinaceum) and tracked disease outcomes. Nutrient addition did not influence infection rates, infection severity or plant biomass. Colletotrichum cereale facilitated R. solani, increasing its infection rate regardless of their order of inoculation. Additionally, simultaneous and C. cereale-first inoculations decreased plant growth and—in plants that did not receive nutrient addition—increased leaf nitrogen concentrations compared to uninoculated plants. These effects were partially, but not completely, explained by the duration and severity of pathogen infections. This study highlights the importance of understanding the intricate associations between the order of pathogen arrival, host nutrient availability and host defence to better predict infection outcomes. [ABSTRACT FROM AUTHOR]

Background: Continuous monocropping obstacles are common in plants, especially medicinal plants, resulting in disease outbreaks and productivity reductions. Foliar disease, mainly caused by Fusarium oxysporum, results in a severe decrease in the yield of Pseudostellaria heterophylla annually. Determining an effective biomethod to alleviate this disease is urgently needed to improve its productivity and quality. Results: This study screened thirty-two keystone bacterial genera induced by pathogens in P. heterophylla rhizosphere soil under continuous monocropping conditions. Pseudomonas, Chryseobacterium, and Flavobacterium, referred to as the beneficial microbiota, were significantly attracted by pathogen infection. The P. palleroniana strain B-BH16-1 can directly inhibit the growth and spore formation of seven primary pathogens of P. heterophylla foliar disease by disrupting fusaric acid production via the emission of volatile organic compounds (VOCs). In addition, strain B-BH16-1 enhances the disease resistance of P. heterophylla by obliterating the pathogen and assembling beneficial microbiota. Conclusion: Pathogen-induced Pseudomonas reshaped phyllosphere microbial communities via direct antagonism of pathogens and indirect disruption of the pathogen virulence factor biosynthesis to enhance disease suppression and improve yields. These results show that inhibiting pathogen virulence biosynthesis to reshape the plant microbial community using disease-induing probiotics will be an innovative strategy for managing plant disease, especially under continuous monoculture conditions. [ABSTRACT FROM AUTHOR]

Maize, as well as other plants, are particularly susceptible to various illnesses. Therefore, one of the most crucial ways for farmers to prevent crop loss is through early diagnosis of plant diseases. The application of deep convolutional neural networks (CNN) for disease identification in maize plants can aid farmers in quickly and reliably detecting the presence of illnesses. In this regard, we have taken 4,988 images belonging to three distinct but widespread types of maize illnesses: Leaf-blight, Common Rust, and Gray Leaf Spot. Moreover, all these images are fed to 13 different pre-trained CNN models such as Alexnet, Densenet201, Googlenet, InceptionresnetV2, InceptionV3, MobilenetV2, VGG-16, VGG-19, Resnet-18, Resnet-50, Resnet-101, Xception and Shufflenet for training as well as testing. The performance of all these CNN models is recorded with reference to accuracy, specificity, precision, False Positive Rate, F1 Score, MCC, and Kappa. Then the effectiveness of each model is evaluated through multiclass statistical analysis by the IBM SPSS statistics tool to select the most efficient classification model among all above said 13 models. The comparison results show, among all the models, Densenet201 has the highest accuracy with the lowest False Positive Rate (FPR), whereas VGG-19 has the lowest accuracy with the highest False Positive Rate for the identification of mentioned maize diseases. [ABSTRACT FROM AUTHOR]

Kuttanad in India is known as ‘the rice bowl of Kerala’ and renowned for its unique rice cultivation system that is below sea level. However, rice production in this wetland ecosystem is seriously threatened by sheath blight disease caused by the fungus Rhizoctonia solani, which has resulted in a significant decline in grain quality and a 50% loss in yield. In the present study, we isolated two endophytic Bacillus subtilis strains, NIIST B616 and NIIST B627, from the roots of rice plants growing in the Kuttanad region and tested their efficacy in controlling sheath blight disease. We found that rice plants inoculated with a combination of both Bacillus strains and R. solani had a 64.71% lower disease incidence and a 31.02% higher harvest index than plants treated with R. solani alone. An analysis of defence-related enzymes and chemicals revealed that treatment with both isolates together greatly enhanced levels of l-phenylalanine ammonia-lyase (PAL; 2.8 fold), peroxidase (POX; 7.4 fold), polyphenol oxidase (PPO; 4.5 fold), and total phenol (8.5 fold) compared with plants treated solely with R. solani, indicating that these isolates significantly increased plant defence responses through induced systemic resistance (ISR), and this finding was validated through a split-root experiment. More detailed analysis using gas chromatography–tandem mass spectrometry (GC-MS/MS) and liquid chromatography–tandem mass spectrometry (LC-MS/MS) techniques revealed that cyclo-(Pro-Leu) and cyclo-(Pro-Phe) were the major components responsible for the antifungal properties of the two isolates and their capacity to trigger ISR in plants. Thus, the Bacillus strains and their metabolites may be deemed viable alternatives for controlling the prevalence of sheath blight disease in rice plants and augmenting rice production at unique locations such as Kuttanad. [ABSTRACT FROM AUTHOR]

Plants are susceptible to a wide range of diseases when they are growing. One of the crucial difficulties in agriculture is the earlier finding of plant diseases. If the diseases are not detected at the beginning, it may have an undesirable effect on the entire production. To avoid these issues, a blockchain-based hybrid optimized deep learning (DL) approach is devised in this work. The plant leaf images are stored in the blockchain network and the noise level of the images is minimized by the Kalman filter. In image segmentation, the Deep Joint segmentation technique is employed to segment the disease-affected portion of the image. The position and color augmentation are carried out to enhance the size and clarity of the image. Moreover, the statistical and speeded-up robust features (SURF) are extracted in the feature extraction stage. In the first level classification process, the developed hunger game search Archimedes optimization (HGSAO) enabled SpinalNet is employed for classifying the plant type and the second level classification is carried out for multiclass disease identification using the proposed HGSAO optimized SpinalNet. Moreover, the proposed HGSAO with SpinalNet outperformed the accuracy of 0.972, True positive rate (TPR) of 0.963, true negative rate (TNR) of 0.951, false negative rate (FNR) of 0.936 and false positive rate (FPR) of 0. 942. [ABSTRACT FROM AUTHOR]

As the world moves toward a green economy and sustainable agriculture, bacterial viruses or bacteriophages (phages) become attractive biocontrol agents for controlling crop diseases. Effective utilization of phages in farms requires integrated knowledge of crops, pathogens, phages, and surroundings. Phages must encounter environmental fluctuations, including temperature, and must remain infectious for successful bacteria lysis. This work studied a soilborne RSJ2 phage discovered in Thailand, which can eliminate Ralstonia solanacearum, causing bacterial wilt disease in chili. We investigated how phage infectivity and nanomechanics responded to thermal changes. The plaque-based assay showed that the infectivity of the RSJ2 phage was stable within 24–40 °C, an average temperature fluctuation in tropical regions. The structural examination also showed that the phage remained intact. The nanomechanical property of the phage was inspected by the atomic force microscopy-based nanoindentation. The result revealed that the phage stiffness within 24–40 °C was statistically similar (0.05–0.06 N/m). Upon heating at 40 °C for 1, 5, and 10 h and resting at 25 °C, the stiffness of the phage particles increased to 0.09–0.11 N/m (54–83% increase). The stiffness results suggest structural adaptation of the protein subunits as a response to thermal alteration. The study exhibits that the phage structure is highly dynamic and can nanomechanically respond to varying temperatures. The phage stiffness may reveal insight into phage adaptation to environmental factors. Equipped with the knowledge of phage infectivity, structure, and nanomechanics, we can design practical guidelines for effective phage usage in farming and propelling green and safe agriculture. [ABSTRACT FROM AUTHOR]

Plant diseases caused by microbial pathogens pose a severe threat to global food security. Although genetic modifications can improve plant resistance; however, environmentally sustainable strategies are needed to manage plant diseases. Nano-enabled immunomodulation involves using engineered nanomaterials (ENMs) to modulate the innate immune system of plants and enhance their resilience against pathogens. This emerging approach provides unique opportunities through the ability of ENMs to act as nanocarriers for delivering immunomodulatory agents, nanoprobes for monitoring plant immunity, and nanoparticles (NPs) that directly interact with plant cells to trigger immune responses. Recent studies revealed that the application of ENMs as nanoscale agrochemicals can strengthen plant immunity against biotic stress by enhancing systemic resistance pathways, modulating antioxidant defense systems, activating defense-related genetic pathways and reshaping the plantassociated microbiomes. However, key challenges remain in unraveling the complex mechanisms through which ENMs influence plant molecular networks, assessing their long-term environmental impacts, developing biodegradable formulations, and optimizing targeted delivery methods. This review provides a comprehensive investigation of the latest research on nanoenabled immunomodulation strategies, potential mechanisms of action, and highlights future perspectives to overcome existing challenges for sustainable plant disease management. [ABSTRACT FROM AUTHOR]

Tomato disease image recognition plays a crucial role in agricultural production. Today, whilemachine vision methods based on deep learning have achieved some success indiseaserecognition, theystill faceseveralchallenges. These include issues such as imbalanced datasets, unclear disease features, small inter-class differences, and large intra-class variations. To address these challenges, this paper proposes a method for classifying and recognizing tomato leaf diseases based on machine vision. First, to enhance the disease feature details in images, a piecewise linear transformation method is used for image enhancement, and oversampling is employed to expand the dataset, compensating for the imbalanced dataset. Next, this paper introduces a convolutional block with a dual attention mechanismcalled DAC Block, which is used to construct a lightweight model named LDAMNet. The DAC Block innovatively uses Hybrid Channel Attention (HCA) and Coordinate Attention (CSA) to process channel information and spatial information of input images respectively, enhancing the model's feature extraction capabilities. Additionally, this paper proposes a Robust Cross-Entropy (RCE) loss function that is robust tonoisylabels, aimedat reducing the impact of noisy labels on the LDAM Net model during training. Experimental results show that this method achieves an average recognition accuracy of 98.71% on the tomato disease dataset, effectively retaining disease information in images and capturing disease areas. Furthermore, the method also demonstrates strong recognition capabilities on rice crop disease datasets, indicating good generalization performance and the ability to function effectively in disease recognition across different crops. The research findings of this paper provide new ideas and methods for the field of crop disease recognition. However, future research needs to further optimize the model's structure and computational efficiency, and validate its application effects in more practical scenarios. [ABSTRACT FROM AUTHOR]

As the Earth warms, alternatives to traditional farming are crucial. Exploring fungi, especially poly extremophilic and extremotolerant species, to be used as plant probiotics, represents a promising option. Extremophilic fungi offer avenues for developing and producing innovative biofertilizers, effective biocontrol agents against plant pathogens, and resilient enzymes active under extreme conditions, all of which are crucial to enhance agricultural efficiency and sustainability through improved soil fertility and decreased reliance on agrochemicals. Yet, extremophilic fungi's potential remains underexplored and, therefore, comprehensive research is needed to understand their roles as tools to foster sustainable agriculture practices amid climate change. Efforts should concentrate on unraveling the complex dynamics of plant-fungi interactions and harnessing extremophilic fungi's ecological functions to influence plant growth and development. Aspects such as plant's epigenome remodeling, fungal extracellular vesicle production, secondary metabolism regulation, and impact on native soil microbiota are among many deserving to be explored in depth. Caution is advised, however, as extremophilic and extremotolerant fungi can act as both mitigators of crop diseases and as opportunistic pathogens, underscoring the necessity for balanced research to optimize benefits while mitigating risks in agricultural settings. Understanding fungal-plant interactions is vital to foster sustainable agriculture practices amidst climate change. Extremophilic fungi's potential as plant probiotics can be crucial to increase crop yields while reducing dependence on toxic agrochemicals. However, the benefits and risks of extremophilic fungi used to promote plant growth and development should be carefully considered. [ABSTRACT FROM AUTHOR]

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods. [ABSTRACT FROM AUTHOR]

Summary: Leaf mould, caused by Fulvia fulva, is a devastating disease of tomato plants. In many commercial tomato cultivars, resistance to this disease is governed by the Cf‐9 locus, which encodes five paralogous receptor‐like proteins. Two of these proteins confer resistance: Cf‐9C recognises the previously identified F. fulva effector Avr9 and provides resistance during all plant growth stages, while Cf‐9B recognises the yet‐unidentified F. fulva effector Avr9B and provides mature plant resistance only. In recent years, F. fulva strains have emerged that can overcome the Cf‐9 locus, with Cf‐9C circumvented through Avr9 deletion. To understand how Cf‐9B is circumvented, we set out to identify Avr9B.Comparative genomics, transient expression assays and gene complementation experiments were used to identify Avr9B, while gene sequencing was used to assess Avr9B allelic variation across a world‐wide strain collection.A strict correlation between Avr9 deletion and resistance‐breaking mutations in Avr9B was observed in strains recently collected from Cf‐9 cultivars, whereas Avr9 deletion but no mutations in Avr9B were observed in older strains.This research showcases how F. fulva has evolved to sequentially break down the Cf‐9 locus and stresses the urgent need for commercial tomato cultivars that carry novel, stacked resistance genes active against this pathogen. [ABSTRACT FROM AUTHOR]

Biofertilizers are environmentally friendly compounds that can enhance plant growth and substitute for chemically synthesized products. In this research, a new strain of the bacterium Bacillus velezensis, designated JZ, was isolated from the roots of strawberry plants and exhibited potent antagonistic properties against Bacillus altitudinis m-1, a pathogen responsible for leaf spot disease in strawberry. The fermentation broth of JZ exerted an inhibition rate of 47.43% against this pathogen. Using an optimized acid precipitation method, crude extracts of lipopeptides from the JZ fermentation broth were obtained. The crude extract of B. velezensis JZ fermentation broth did not significantly disrupt the cell permeability of B. altitudinis m-1, whereas it notably reduced the Ca2+-ATPase activity on the cell membrane and markedly elevated the intracellular reactive oxygen species (ROS) concentration. To identify the active compounds within the crude extract, QTOF-MS/MS was employed, revealing four antimicrobial compounds: fengycin, iturin, surfactin, and a polyene antibiotic known as bacillaene. The strain JZ also produced various plant-growth-promoting substances, such as protease, IAA, and siderophore, which assists plants to survive under pathogen infection. These findings suggest that the JZ strain holds significant potential as a biological control agent against B. altitudinis, providing a promising avenue for the management of plant bacterial disease. [ABSTRACT FROM AUTHOR]