Bacterial Blight Disease Research Articles

Genetic diversity and pathotype profiling of Xanthomonas oryzae pv. oryzae isolates from diverse rice growing ecosystems of Karnataka state of India

Bacterial leaf blight (BLB) disease of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the significant constraints for rice production. A study was carried out to analyze the virulence, genetic diversity, and population structure of Xoo isolates collected from different rice ecosystems of Karnataka, India. The taxonomic identity of all 54 isolates was confirmed using a Xoo-specific PCR assay. The virulence assay on a set of twenty-eight near-isogenic differentials identified nine pathotype groups (KPthX-1 to KPthX-9). Among them, KPthX-6 was highly virulent, whereas KPthx-2 was less virulent. Differentials with four and five resistance (R) genes (Xa4+xa5+xa13+Xa21, Xa4+Xa7+xa13+Xa21, and Xa4+xa5+Xa7+xa13+Xa21) were effective against all the pathotypes. Further, genetic diversity was deduced using a universal marker set, JEL-1, and JEL-2, complementary to a conserved repetitive sequence IS1112. The amplicon size and numbers varied from 100 bp to 3 Kb and 1 to 25. Based on the amplicon counts, Jaccard’s co-efficient and phylogenetic analyses were carried out and categorized the 54 isolates into three clusters (I to III). Cluster-II contains the maximum number of isolates (27), followed by cluster-I (23 isolates) and cluster-III (one isolate). The structure analysis categorized the isolates into five subgroups, viz. SG1, SG2, SG3, SG4, and SG5 indicate the existence of five subpopulations. The present study has identified the genetic and virulence diversity of the Xoo population and reported the effective R gene/s for different rice ecosystems, which can be used in the breeding program for the eco-specific management of BLB of rice.

First report of Burkholderia gladioli causing bacterial panicle blight of rice in Malaysia.

Rice (Oryza sativa) is a staple food for most of the world's populations, particularly in Asia (Gumma et al. 2011). The rice sector provides Malaysians with a food supply, food sufficiency, and income for growers (Man et al. 2009). From January to February 2022, panicle samples showing symptoms of bacterial panicle blight (BPB) disease, including reddish-brown, linear lesions with indistinct margins on flag-leaf sheaths and blighted, upright, grayish straw-colored florets with sterile and aborted grains on panicles were collected in granary areas in Sekinchan, Selangor, Malaysia with 90% disease incidence in fields. Surface-sterilization of infected leaf tissue was performed using 75% ethanol and 1% sodium hypochlorite, followed by rinsing three times in sterilized water. Leaf tissue was macerated in sterilized water and aliquots were spread on King's B agar medium, then cultured for 24 h to 48 h at 35 °C. All isolated bacteria were Gram-negative rods, positive for catalase and gelatinase but negative for indole, oxidase and hydrogen sulfide (H2S), and utilized sucrose, inositol, mannitol, glucose, and citrate. Colonies were circular and smooth-margined, producing a diffusible yellowish-green pigment on King's B agar medium, which are characteristics of Burkholderia species (Keith et al. 2005). Five representative isolates (UPMBG7, UPMBG8, UPMBG9, UPMBG15, UPMBG17) were selected for molecular and pathogenicity tests. PCR using specific primers targeting the gyrB gene for molecular characterization was performed, and the ∼470 bp amplicons were sequenced (Maeda et al. 2006) and deposited in GenBank (OM824438 to OM824442). A BLASTn analysis revealed that the five isolates were 99% identical to the B. gladioli reference strains MAFF 302533, GRBB15041, and LMG19584 in GenBank (AB190628, KX638432, and AB220898). A phylogenetic tree using Maximum-likelihood analysis of the gyrB gene sequences showed that the five isolates were 99% identical to B. gladioli reference strains (AB190628, KX638432, and AB220898). To verify the identification of these isolates, the 16S rDNA gene was amplified using 16SF/16SR primers (Ramachandran et al. 2021), producing ~1,400 bp amplicons. The resulting sequences of the five isolates (OM869953 to OM869957) were 98% identical to the reference strains of B. gladioli (NR113629 and NR117553). To confirm pathogenicity, 10 ml suspensions of the five isolates at of 108 CFU/ml were inoculated into the panicles and crowns of 75-day-old rice seedlings of local rice varieties MR269 and MR219 grown in a glasshouse with temperatures ranging from 37 °C to 41 °C (Nandakumar et al. 2009). Control rice seedlings were inoculated with sterilized water. All isolates produced BPB disease symptoms like those originally found in the rice fields at four weeks after inoculation. Control seedlings remained asymptomatic. To fulfill Koch's postulates, the bacteria were reisolated from symptomatic panicles and were confirmed as B. gladioli by sequence analysis of the gyrB and 16S rDNA genes. To our knowledge, this is the first report of B. gladioli causing BPB disease of rice in Malaysia. Since BPB disease causes a significant threat to the rice industry, it is crucial to investigate the diversity of this destructive pathogen for effective disease management strategies in Malaysia.

Molecular Breeding for Incorporation of Submergence Tolerance and Durable Bacterial Blight Resistance into the Popular Rice Variety 'Ranidhan'.

Ranidhan is a popular late-maturing rice variety of Odisha state, India. The farmers of the state suffer heavy loss in years with flash floods as the variety is sensitive to submergence. Bacterial blight (BB) disease is a major yield-limiting factor, and the variety is susceptible to the disease. BB resistance genes Xa21, xa13, and xa5, along with the Sub1 QTL, for submergence stress tolerance were transferred into the variety using marker-assisted backcross breeding approach. Foreground selection using direct and closely linked markers detected the progenies carrying all four target genes in the BC1F1, BC2F1, and BC3F1 generations, and the positive progenies carrying these genes with maximum similarity to the recipient parent, Ranidhan, were backcrossed into each segregating generation. Foreground selection in the BC1F1 generation progenies detected all target genes in 11 progenies. The progeny carrying all target genes and similar to the recipient parent in terms of phenotype was backcrossed, and a total of 321 BC2F1 seeds were produced. Ten progenies carried all target genes/QTL in the BC2F1 generation. Screening of the BC3F1 progenies using markers detected 12 plants carrying the target genes. A total of 1270 BC3F2 seeds were obtained from the best BC3F1 progeny. Foreground selection in the BC3F2 progenies detected four plants carrying the target genes in the homozygous condition. The bioassay of the pyramided lines conferred very high levels of resistance to the predominant isolates of bacterial blight pathogen. These BB pyramided lines were submergence-tolerant and similar to Ranidhan in 13 agro-morphologic and grain quality traits; hence, they are likely to be adopted by farmers.

Performance and Pest and Disease Resistance Tests Local Genotype of Lowland Rice

Rice is a food commodity with economic value as the main staple to maintain national and global food stability. The need for rice is in genotype with population growth, so it is necessary to increase production and productivity. The development of superior varieties requires genetic resources from local rice genotypes from West Sumatra regions, including Marapulai, Silih Baganti, Bujang Marantau, Hitam Manih, Junjuang, Mundam, Papanai and so on. Information on plant performance tests related to growth, agronomic components, and resistance to pests and diseases of local genotypes of lowland rice is still very limited. The experiment aimed to obtain local genotypes of lowland rice that had the appearance and resistance to pests and diseases. The observed growth and yield components were planting height, number of productive tillers, flowering age, ripening age, panicle length, number of seeds per panicle, number of good seeds per panicle, 1000 grain weight, and dry grain weight per ha. Pest and disease resistance variables are; brown planthopper pests, bacterial leaf blight and blast disease. Observational data were analyzed by F test at 5% level with the Least Significant Difference (LSD) at 5%. Analysis using PC software program Statistix ver 8.0. Collected data are observing pest and disease resistance based on assessment standards from the International Rice Research Institute. Based on the growth and agronomic components of the experiment, Maraplai was one of the genotypes that could be used as a superior variety, with the resistance of brown planthopper biotypes 1, 2 and 3 to making them susceptible. Marapulai local genotype has better growth and yield characteristics, as well as resistance to pests and diseases.

First report of rice bacterial leaf blight disease caused by Pantoea ananatis in the United States.

In August 2021, bacterial leaf blight-like symptoms were observed on 14 out of 570 rice genotypes (Oryza sativa) in research field plots of global rice germplasm grown in Arkansas (eXtra Figure S1. A & B). The disease was characterized by spreading lesions on leaves, panicle sterility and reduced yield in highly susceptible, mature rice germplasm. No spread of disease to nearby plants was observed. Isolations were performed at Colorado State University, where soakates from symptomatic leaves were spread onto nutrient agar. After 72 h at 28°C, uniform, distinct, yellow-colored bacterial colonies were observed. To screen for the presence of common rice bacterial pathogens, PCR amplification directly from colonies or from DNA isolated from symptomatic field-collected leaves was performed. Primers specific for Xanthomonas oryzae pvs. oryzae and oryzicola (Lang et al., 2010), Burkholderia glumae (Echeverri-Rico et al., 2021), and Pseudomonas fuscovaginae (Ash et al., 2014) did not amplify indicating these organisms were not present. Sequencing of 16S rRNA gene (Weisburg et al., 1991) amplicons suggested the bacteria belonged to the genera Pantoea and Sphingomonas (NCBI accession no. OP683332 and OP683333, respectively). Amplicons resulting from primers specific to the gyrB gene region of P. ananatis (Kini et al., 2021) were sequenced and the fragment was compared to the P. ananatis PA13 reference genome using a BLAST analysis. One candidate (AR358) showed 100% identity with the P. ananatis gyrB region. Primers specific for Sphingomonas sp. (Bangratz et al., 2020) confirmed the second candidate (AR359) as a Sphingomonas sp. The identity of P. ananatis was confirmed by the Plant Pathogen Confirmatory Diagnostics Laboratory (Beltsville, MD, USA). To determine pathogenicity, leaves from 7-day-old seedlings of rice (Oryza sativa) cultivar Kitaake were scissor-clip inoculated (Kauffman et al., 1973) with four different treatments and compared to control leaves inoculated with sterile water. Treatments for the experiment consisted of bacterial suspensions (108 CFU/ml) of the two candidate organisms, P. ananatis (strain AR358) or Sphingomonas sp. (strain AR359), individually or in a 1:1 ratio of P. ananatis:Sphingomonas sp., or soakate from infected field tissue. Lesions similar to those observed in the field were only detected on leaves inoculated with P. ananatis or infected field tissue soakate at 7-days post-inoculation (eXtra Figure S1. C). Bacteria were recovered from the leaves of the artificially inoculated seedlings from three treatments (P. ananatis, P. ananatis:Sphingomonas sp. and soakate from the infected field tissue) and were determined to be P. ananatis based on colony morphology, amplification of 16s rRNA, and gyrB sequence data. Our results confirm the pathogenicity of P. ananatis to rice and fulfill Koch's postulates. P. ananatis was also recovered from several similarly diseased rice breeding lines at the University of Arkansas System Division of Agriculture Rice Research and Extension Center. We conclude that P. ananatis is the causal pathogen for leaf blight-like symptoms observed in the global rice cultivars grown in Arkansas. P. ananatis was previously reported as a pathogen on rice in several rice growing regions, including China (Yu et al., 2021), India (Reshma et al., 2022), and Africa (Kini et al., 2017), however, this is the first report of P. ananatis as a pathogen of rice in the United States.

Effectiveness of Binahong (Anredera cordifolia) Leaf Extract in Suppressing The Growth of Xanthomonas oryzae and The Occurrence of Bacterial Leaf Blight Disease in Rice Plant (Oryza sativa)

Effectiveness of Binahong (Anredera cordifolia) Leaf Extract in Suppressing The Growth of Xanthomonas oryzae and The Occurrence of Bacterial Leaf Blight Disease in Rice Plant (Oryza sativa)

Seed treatments and seedling root soaking in biological agents improved plant growth and reduced bacterial leaf blight disease incidence in rice

Bacterial leaf blight (BLB) is caused by Xanthomonas oryzae pv. oryzae (Xoo), a seed-soil-air-borne pathogen that can reduce 60% of rice yield. The primary study intended to investigate the effect of seed treatments and root soaking with biological agents (a mixture of Pseudomonas diminuta and Bacillus subtilis) on plant growth and the severity of BLB in a greenhouse. A split-plot experimental design was used with seed treatments as the main plot: negative control, positive control (Xoo inoculated), bactericide (streptomycin sulfate 20%) 0.2%, biopriming with biological agents 108 CFU ml−1, matriconditioning plus bactericide 0.2%, biomatriconditioning with biological agents 108 CFU ml−1. The subplots were root soaking: untreated, bactericide 0.1%, 0.2%, 0.4%, 0.6%; biological agents 106 CFU ml−1, 108 CFU ml−1, and 1010 CFU ml−1. Biomatriconditioning reduced the BLB incidence three weeks after sowing from 10.6% to 1.7% and increased plant height four weeks after transplanting (WAT). Biomatriconditioning followed by root soaking in the biological agents or 0.4 % bactericide increased plant dry weight 3 WAT. Biomatriconditioning or root soaking in the biological agents (106 CFU ml−1) provided the most significant number of tillers 4 WAT.

A Morphological Change in Leaves-Based Image Processing Approach for Detecting Plant Diseases

In recent years, rice production is mostly affected by rice plant leaf diseases due to the unawareness of suitable management strategies. The paddy leaves are regularly impacted by Brown spot and Bacterial blight diseases, which result in creating major loss to the farm owners. The naked-eye observation is used by the farmer to analyse the condition of paddy leaves, but, it takes more time and the accuracy of it is based on the observer. The naked-eye observation is generally difficult and it has a high possibility of human error. To overcome these drawbacks, a fast and suitable recognition system is required. Thus, appropriate methodologies are required for the determination of diseases in paddy leaf. The use of image processing is seen as a non-intrusive method that offers farmers a precise, economical, and trustworthy solution. Therefore, this research work, focused to provide the fast recognition system to detect leaf diseases in paddy crops.

Management of bacterial leaf blight disease of rice in farmer's field condition at Bhaktapur district of Nepal

Bacterial leaf blight (BLB) caused by Xanthomonas oryzae pv. oryzae is considered as one of the major threats to rice production in Nepal. To identify the effective control measures, an experiment was done in a randomized complete block design with three replications in a farmer's rice field at Bhaktapur, during the summer seasons of 2018 and 2019. Three plants extracts; 1% Neem extract, 2% Asuro leaf extract, and 2% Titepati leaf extract along with two fungicides viz. 0.1% Agricin and 0.2% Blitox 50, and one bio-control agent 1%Kisan Pseudomonas were used as treatments. The first spray was given as a prophylactic spray at the jointing stage, and they were applied 3 times at 50, 60 and 70 days interval. All the treatments significantly reduced disease and improved test weight higher than control. Among the tested treatments, 0.1% Agricin performed as the best with 65.70% disease control and increase in yield over control by 28.13%. Among the tested botanicals, 2% stock solution of Titepati performed as the best with 50.59% disease control and increase in yield by 13.51%. Therefore, three times application of 0.1% Agricinat 10 day interval starting from jointing stage can be used to control BLB disease for higher rice production.

Pyramiding of Four Broad Spectrum Bacterial Blight Resistance Genes in Cross Breeds of Basmati Rice.

Pyramiding of major resistance (R) genes through marker-assisted selection (MAS) is a useful way to attain durable and broad-spectrum resistance against Xanthomonas oryzae pv. oryzae pathogen, the causal agent of bacterial blight (BB) disease in rice (Oryza sativa L.). The present study was designed to pyramid four broad spectrum BB-R genes (Xa4, xa5, xa13 and Xa21) in the background of Basmati-385, an indica rice cultivar with much sought-after qualitative and quantitative grain traits. The cultivar, however, is susceptible to BB and was therefore, crossed with IRBB59 which possesses R genes xa5, xa13 and Xa21, to attain broad and durable resistance. A total of 19 F1 plants were obtained, some of which were backcrossed with Basmati-385 and large number of BC1F1 plants were obtained. In BC1F2 generation, 31 phenotypically superior genotypes having morphological features of Basmati-385, were selected and advanced up to BC1F6 population. Sequence-tagged site (STS)-based MAS was carried out and phenotypic selection was made in each successive generation. In BC1F6 population, potentially homozygous recombinant inbred lines (RILs) from each line were selected and evaluated on the bases of STS evaluation and resistance to local Xanthomonas oryzae pv. oryzae (Xoo) isolates. Line 23 was found pyramided with all four BB-R genes i.e., Xa4, xa5, xa13 and Xa21. Five genotypes including line 8, line 16, line 21, line 27 and line 28 were identified as pyramided with three R genes, Xa4, xa5 and xa13. Pathological study showed that rice lines pyramided with quadruplet or triplet R genes showed the highest level of resistance compared to doublet or singlet R genes. Thus, line 23 with quadruplet, and lines 8, 16, 21, 27, and 28 with triplet R genes, are recommended for replicated yield and resistance trials before release as new rice varieties. Further, traditional breeding coupled with MAS, is a solid way to attain highly effective BB-resistant rice lines with no yield cost.

The Complete Genome Resource of Xanthomonas oryzae pv. oryzae CIX2779 Includes the First Sequence of a Plasmid for an African Representative of This Rice Pathogen.

The bacterial plant pathogen Xanthomonas oryzae pv. oryzae is responsible for the foliar rice bacterial blight disease. Genetically contrasted, continent-specific, sublineages of this species can cause important damages to rice production both in Asia and Africa. We report on the genome of the CIX2779 strain of this pathogen, previously named NAI1 and originating from Niger. Oxford Nanopore long reads assembly and Illumina short reads polishing produced a genome sequence composed of a 4,725,792-bp circular chromosome and a 39,798-bp-long circular plasmid designated pCIX2779_1. The chromosome structure and base-level sequence are highly related to reference strains of African X. oryzae pv. oryzae and encode identical transcription activator-like effectors for virulence. Importantly, our in silico analysis strongly indicates that pCIX2779_1 is a genuine conjugative plasmid, the first indigenous one sequenced from an African strain of the X. oryzae species. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Assessment of copper (Cu) nanoparticle for their biocontrol activity against Xanthomonas oryzae pv. oryzae, growth promotion, and physiology of rice (Oryza sativa L.) plants.

Among the various biotic factors that disrupt crop yield, Xanthomonas oryzae pv oryzae (Xoo) is the most ruinous microbe of rice and causes bacterial leaf blight (BLB) disease. The present study focused on the utilization of copper nanoparticles (Cu-NPs) to control BLB. The copper nanosuspension (259.7 nm) prepared using Na-CMC, CuSO4·7H2O, and NaOH showed effectively inhibited Xoo (65.0μg/ml). The performance of Cu-NPs in vivo showed enhanced plant attributes (127.9% root length and 53.9% shoot length) compared to the control and CuSO4 treated seedling. Furthermore, Cu-NPs treated seedlings showed 23.01% disease incidence (DI) compared to CuSO4 (85.71%) treated and control plants (91.83%). In addition to enhancing the growth parameters and reducing DI, seed priming with Cu-NPs improved the total chlorophyll content to 36.0% compared to the control. The assessment of antioxidant enzymes such as superoxide dismutase (1.9 U), polyphenol oxidase, peroxidase, and phenylalanine ammonia-lyase (two- to three-fold) in roots and shoots of rice plants revealed significant enhancement in Cu-NPs treated seedlings (P<0.05). The present study suggests that Cu-NPs can be used to control Xoo and enhance rice growth.

Design and preparation of selenized flour: An efficient biomaterial inhibiting the growth of Xoo

Herein, we wish to report a novel method for preparing selenium-containing materials, i.e. the selenization of flour via fermentation. Compared with traditional methods, the fermentation reaction could directly transfer selenium from its inorganic sources into the target material without using any flammable or hazardous chemical reagents. The produced selenized flour could well inhibit the growth of Xoo, the pathogen leading to rice bacterial blight disease. As a selenium-containing material using edible flour as the selenium carrier, it is more bio-compatible than many chemical pesticides. Hydroxyls of starch in selenized flour may facilitate the material adhering onto the rice leaves to allow the continuous release of the efficacy.

Multi-Class Plant Leaf Disease Detection Using a Deep Convolutional Neural Network

Traditional machine learning methods of plant leaf disease detection lack successful performances due to poor feature representation and correlation. This paper presents a novel methodology for automatic plant leaf disease detection using cascaded deep convolutional neural network (CDCNN) which focusses on increasing the feature representation and correlation factors. It provides distinctive features that gives low intra-class variability and higher inter-class variability. CDCNN were performed on a plant-village leaf disease database which consists of 13 classes of tomato, potato, and pepper bell plant diseases; DCNN model performs better with an overall accuracy, recall, and precision of 98.50%, 0.98, and 0.97 respectively. Additionally, performance of the proposed algorithm is evaluated on real time cotton leaf database for bacterial blight, leaf miner, and spider mite diseases detection and provides 99.00% accuracy. The proposed DCNN outperforms well compared to traditional machine learning and deep learning models and is able to detect the diseases present in the leaves of the plant.

Editing of the rice importin gene IMPα1b results in sequestration of TAL effectors from plant cell nuclei

Transcription activator-like effectors (TALEs) produced by plant pathogenic bacteria mainly belonging to the genus Xanthomonas cause plant diseases through activation of host susceptibility genes in plant cell nuclei. How TALEs enter plant cell nuclei was not clear until recent studies about PthXo1 and TALI, two TALEs produced by Xanthomonas oryzae pv. oryzae (Xoo) and X. oryzae pv. oryzicola (Xoc), the rice (Oryza sativa) pathogens that cause bacterial blight and bacterial leaf streak, respectively. Here, we report that rice importin IMPα1b serves as a nuclear transport receptor in rice plants to facilitate the nuclear import of PthXo1 and TALI from Xoo and Xoc, respectively. While wild-type (WT) rice plants support the nuclear import of PthXo1 and TALI, nuclear trafficking is defective in OsIMPα1b loss-of-function mutants generated by clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR-associated 9 (Cas9)-based gene editing. In the edited plants infected by Xoo, OsIMPα1b sequesters PthXo1 from the nucleus, the PthXo1-targeted rice susceptibility gene is no longer activated, and bacterial virulence and blight disease are alleviated as a result. In the edited plants infected by Xoc, OsIMPα1b sequesters TALI from the nucleus, the role of TALI in suppressing rice defense responses is nullified, and rice defense responses are in turn activated to inhibit bacterial virulence and alleviate bacterial leaf streak severity.

A novel Moore-Penrose pseudo-inverse weight-based Deep Convolution Neural Network for bacterial leaf blight disease detection system in rice plant

The vital food crop in the agriculture field is rice, but rice growing is impacted by numerous maladies. The Bacterial Leaf Blight (BLB) disease influences rice standards. Prevailing research practices have less accuracy and could not surmount the noise of the images. This paper proposed Moore-Penrose pseudo-inverse Weight-related Deep Convolutional Neural Network (MPW-DCNN) overcomes such complications for the BLB disease identification system. The proposed method encompasses six phases. First, the Image Acquisition (IA) procedure is accomplished, next the pre-processing is carried out, in which the noise of the input rice leaf image is mitigated using the Hybrid Gaussian-Weiner (HGW) filter, and the image pixel is normalized by utilizing min-max normalization. Then, the segmentation of the input image is done by Improved Fuzzy C-Means (IFCM). Next, the features, such as entropy, energy, correlation, contrast, homogeneity, colour histogram, and Scale-Invariant Feature Transforms (SIFT) are extracted from the segmented image. Then, the efficient features are chosen by applying the Exhaustiveness and Brownian Motion-related Elephant Herding Optimization (EBM-EHO) algorithm. Then, these selected features are fed to the MPW-DCNN classifier, which categorizes the image as 'BLB malady' or ‘normal’ or else 'chances being influenced by other maladies'. Lastly, the proposed MPW-DCNN performance is equated with the prevailing classification algorithm, and better accuracy is proffered by the proposed work than the prevalent algorithms. The accuracy of the implemented approach is 2.36, 3.08, 4.62, 5.13, and 6.15% improved than the existing methods, such as Support Vector Machine (SVM) + Deep features, AlexNet, Deep Convolutional Neural Network (DCNN), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), respectively.

Bioformulation Development of Bacillus velezensis Strain N1 to Control Rice Bacterial Leaf Blight

Bacterial leaf blight is a serious disease of rice caused by Xanthomonas oryzae pv. oryzae (Xoo). The objective of this study was to develop a bioformulation of Bacillus velezensis strain N1 by using microencapsulation techniques through spray drying and to then evaluate its performance in controlling rice bacterial leaf blight under greenhouse conditions. Three formulations with a different main carrier were studied and the viability after spray drying, moisture content, characteristic of formulation powder under scanning electron microscope and storage stability were examined. The results showed that formulation 3 (10 % skimmed milk, 20 % maltodextrin, 0.06 % sodium alginate and 5 % tapioca starch) had the highest antagonistic bacterial viability after spray drying of 1.24×107 CFU/g. Formulation 2 had the highest antagonistic bacterial viability of 1.44×106 CFU/g and had the lowest viability reduction of 0.61 Log CFU/g after storage at room temperature for 3 months. Moisture content of each formulation was less than 4 %. Among the 3 formulations of strain N1 bacteria against Xoo, formula 2 was able to control rice bacterial blight disease with the highest disease reduction index (45.87 %). All the results indicated that Bacillus velezensis strain N1 can be an effective antagonist and could potentially be used to develop a more successful bioformulation against rice bacterial leaf blight.&#x0D; GRAPHICAL ABSTRACT

Bacillus spp.-Mediated Growth Promotion of Rice Seedlings and Suppression of Bacterial Blight Disease under Greenhouse Conditions.

Rice (Oryza sativa L.) is a major cereal and staple food crop worldwide, and its growth and production are affected by several fungal and bacterial phytopathogens. Bacterial blight (BB) is one of the world's most devastating rice diseases, caused by Xanthomonas oryzae pv. oryzae (Xoo). In the current study, Bacillus atrophaeus FA12 and B. cabrialesii FA26 were isolated from the rice rhizosphere and characterized as having broad-range antifungal and antibacterial activities against various phytopathogens, including Xoo. In addition, the selected strains were further evaluated for their potent rice growth promotion and suppression efficacy against BB under greenhouse conditions. The result shows that FA12 and FA26, applied as seed inoculants, significantly enhanced the vigor index of rice seedlings by 78.89% and 108.70%, respectively. Suppression efficacy against BB disease by FA12 and FA26 reached up to 59.74% and 54.70%, respectively, in pot experiments. Furthermore, MALDI-TOF MS analysis of selected strains revealed the masses ranged from m/z 1040 to 1540, representing that iturins and fengycin are the major antimicrobial compounds in the crude extracts, which might have beneficial roles in rice defence responses against BB. In conclusion, FA12 and FA26 possess broad-range antagonistic activity and have the capability to promote plant growth traits. More importantly, applying these strains has a high potential for implementing eco-friendly, cost-effective, and sustainable management practices for BB disease.

Detection of Xanthomonas oryzae pv oryzae in BLB Paddy Seed Samples from Endemic Regions of Telangana State, India

Bacterial Leaf Blight disease (BLB) in rice caused by Xanthomonas oryzae pv oryzae (Xoo), is one of the most economically important diseases causing epidemics globally, resulting in severe crop losses of 50% in addition to affecting the seed quality in Asia. In the present study, ten BLB affected paddy seed samples were collected during Kharif, 2021-22 from the farmer's fields of major BLB endemic districts viz., Nizamabad and Khammam of Telangana state. The collected BLB seed samples were subjected to different standard seed health tests (ISTA, 1996) for detection and isolation of Xoo. Among the different methods evaluated, the detection of Xoo from BLB affected seed samples was high in the Agar plate method followed by the test tube water agar method. However, the component plating method revealed that, of the different seed components, the per cent recovery of Xoo was high from the embryo followed by lemma, palea and endosperm indicating the systemic type of infection of Xoo. Further, the results of the paper towel method stated that of the ten BLB affected paddy seed samples collected, the seed samples of Govuru village (Nizamabad district) and Rejarla village (Khammam district) have recorded below Indian Minimum Seed Certification Standards i.e 77.00% and 79.25% of per cent seed germination compared to the seed samples of other locations.

Early Detection of Bacterial Blight in Hyperspectral Images Based on Random Forest and Adaptive Coherence Estimator

Rice disease detection is of great significance to rice disease management. It is difficult to identify the rice leaves with different colors in different disease periods by RGB image and without aided eyes. Traditional equipment and methods are relatively inefficient in meeting the needs of current disease detection. The accurate and efficient detection the infected areas from hyperspectral images has become a primary concern in current research. However, current spectral target detection research pays less attention to the time and computing resources consumed by detection. A disease detection method based on random forest (RF) and adaptive coherence estimator (ACE) is proposed here. Firstly, based on the spectral differences between diseased and healthy leaves, 18 characteristic spectral wavelengths with the highest importance were selected by an RF algorithm, and the spectral images of those characteristic wavelengths were synthesized. Then, the ACE model was established for the disease recognition of full wavelength spectral images, characteristic wavelength spectral images, and RGB images. At the same time, three other familiar target detection methods were selected as the control experiments. The detection results showed a similarity between the detection performance of the four detection methods for full wavelength spectral image and characteristic wavelength spectral image. This detection performance was higher than that of the RGB image, indicating that characteristic wavelength spectral image can replace full wavelength spectral image for disease detection. The detection performance of the ACE algorithm was better than other algorithms. The detection accuracy of 18 characteristic wavelengths was 97.41%. Compared with the hyperspectral full wavelength image detection results, the accuracy decreased by 1.12%, and the detection time decreased by 2/3, which greatly reduced the detection time. Based on these results, the target detection method combining the RF algorithm and the ACE algorithm can effectively and accurately detect rice bacterial blight disease, which provides a new method for automatic detection of plant disease in the field.