Rice Brown Spot Research Articles

Integrated use of Propiconazole, Pseudomonas fluorescens and Bacillus subtilis for the management of rice brown leaf spot

The present investigation was carried out to study the effect of integrated use of propiconazole and bacterial bio control agents (Pseudomonas fluorescens and Bacillus subtilis) on the incidence of rice (Oryza sativa L) brown leaf spot during 2020-2021 at Annamalai University, Chidambaram. Among the various treatments tested in pot culture, seed treatment with consortia of P. fluorescens and B. subtilis + 1st foliar spray with Consortia of P. fluorescens and B. subtilis at 45 DAT + 2nd foliar spray with Propiconazole 25% EC at 60 DAT (T7) recorded minimum disease incidence (7.45%) and maximum number of productive tillers/ clump (17), grain (76.0 g/pot) and straw (127 g/pot) yield. In field studies also T7 treatment produced least disease incidence of brown spot (8.50%) and maximum growth parameters (17 productive tillers/clump) grain (74 qha-1) straw (86.30 qha-1) yield. Per cent disease incidence increased regularly with age of the crop, irrespective of treatments and maximum values were recorded at harvest. The maximum disease incidence and minimum values of growth and yield of rice recorded under control in both pot culture and field condition.

Integrated use of Propiconazole, Pseudomonas fluorescens and Bacillus subtilis for the management of rice brown leaf spot

The present investigation was carried out to study the effect of integrated use of propiconazole and bacterial bio control agents (Pseudomonas fluorescens and Bacillus subtilis) on the incidence of rice (Oryza sativa L) brown leaf spot during 2020-2021 at Annamalai University, Chidambaram. Among the various treatments tested in pot culture, seed treatment with consortia of P. fluorescens and B. subtilis + 1st foliar spray with Consortia of P. fluorescens and B. subtilis at 45 DAT + 2nd foliar spray with Propiconazole 25% EC at 60 DAT (T7) recorded minimum disease incidence (7.45%) and maximum number of productive tillers/ clump (17), grain (76.0 g/pot) and straw (127 g/pot) yield. In field studies also T7 treatment produced least disease incidence of brown spot (8.50%) and maximum growth parameters (17 productive tillers/clump) grain (74 qha-1) straw (86.30 qha-1) yield. Per cent disease incidence increased regularly with age of the crop, irrespective of treatments and maximum values were recorded at harvest. The maximum disease incidence and minimum values of growth and yield of rice recorded under control in both pot culture and field condition.

Measuring plant disease severity in R: introducing and evaluating the pliman package

Image analysis based on color thresholding is the reference method for measuring severity as percent area affected. It is deemed to produce accurate results, usually considered the “true” severity value. More than a dozen applications have been used for the task in phytopathometry studies, but none was coded in R language. Here we introduced and evaluated pliman, a suite for the analysis of plant images. In particular, we show functions for computing percent severity based on RGB information contained in image palettes prepared by the user. Six image collections, totaling 249 images, from different diseases (wheat tan spot, soybean rust, olive leaf spot, rice brown spot, bean angular spot, and Xyllela fastidiosa on tobacco) exhibiting a range of symptomatic patterns and severity were used to evaluate the agreement of pliman predictions with measures by three other software: APS Assess, LeafDoctor, and ImageJ. Three users independently prepared three image palettes (each representing leaf background, symptomatic, or healthy leaf tissue) by manually inspecting and subsetting these target areas of the images. Pliman predictions by a joint palette (by joining images by the three users into one) were highly concordant (ρc > 0.98) with measures by the other software for all but Xylella fastidiosa on tobacco (ρc = 0.49). The error for the latter may be due to the low contrast between symptomatic and healthy tobacco tissues. Users showed to be a source of variation in the overall concordance depending on the disease. Reduction in the image resolution (< 1 megapixel) did not impact the results. Combined with parallel processing, the use of low image resolution (1078 × 680) decreased processing time, resulting in pliman being ~ 150 to ~ 700 times faster than existing tools for disease quantification. Pliman showed great potential to produce accurate measures and accelerate studies involving plant disease severity measurements, especially for the batch processing of large sets of image collections.

Molecular Identification of Rice Brown Leaf Spot Fungus Using Conserved Region of Glycoside hydrolase Gene

Rice brown leaf spot disease is one of the most devastating fungal diseases that affect rice plants and reduces the yield across the globe. In the present study, twenty isolates from different rice-growing districts in Tamil Nadu by tissue segment approach was isolated from disease-infected leaves and a purified. The isolates were identified at the genus level using ITS primers, which resulted in an amplicon size of a 550 bp. With the use of species-specific primers of BF and BR the isolate was confirmed as B. oryzae. Gene-specific primers were designed using Primer 3 plus software for the Glycoside hydrolase family 13 protein, which is a highly conserved gene in B. oryzae. All isolates were sequenced using gene-specific primers, and phylogenetic tree analysis was used to investigate the isolate's diversity. The results revealed considerable variance among the B. oryzae collected from different locations of Tamil Nadu with other B .oryzae isolates retrieved from the NCBI database. The isolates were phenotypically validated using morphological and cultural features

Efficacy of the antagonistic fungus Talaromyces tratensis KUFA 0091 in controlling rice blast and brown leaf spot diseases in field trials

Efficacy of the antagonistic fungus Talaromyces tratensis KUFA 0091 in controlling rice blast and brown leaf spot diseases in field trials

Predicting rice diseases across diverse agro-meteorological conditions using an artificial intelligence approach.

With the aid of a plant disease forecasting model, the emergence of plant diseases in a given region can be predicted ahead of time. This makes it easier to take proactive steps to reduce losses before they occur. The proposed model attempts to find an association between agrometeorological parameters and the occurrence of the four types of rice diseases. Rice is the staple food of people in Maharashtra. The four major diseases that occur on rice crops are focused on this paper (namely Rice Blast, False Smut, Bacterial Blight and Brown Spot) as these diseases spread rapidly and lead to economic loss. This research paper demonstrates the usage of artificial neural network (ANN) to detect, classify and predict the occurrence of rice diseases based on diverse agro-meteorological conditions. The results were carried out on two cases of dataset split that is 70–30% and 80–20%. The various types of activation function (AF) such as sigmoid, tanH, ReLU and softmax are implemented and compared based on various evaluation metrics such as overall Accuracy, Precision, Recall and F1 score. It can be concluded that the softmax AF applied to 70–30% split of dataset gives the highest accuracy of 92.15% in rice disease prediction.

Assessment of Genetic Parameter of Variability and Heritability based on Morphological Traits and Disease Parameters for Brown Spot in Rice (Oryza sativa L.)

Cochliobolus miyabeanus is a serious threat to the standing rice crop in context of production and productivity as it results in loss of both grain quality and yield. The pathogen causes brown spot disease in rice which had resulted in two severe famines in past. Hence, in this regard it is imperative to search for new and diverse resistance sources and to evaluate them with respect to genetic variability and inherent genetic potential for various morphological traits including yield and yielding attributing traits and disease estimating parameters for identifying high yielding diverse resistant lines that could be utilized in future breeding programmes aimed at development of superior cultivars against brown spot disease. Keeping this in view this study was conducted at Rice Research Farm, RPCAU, Pusa to evaluate 300 genotypes for rice for various morphological traits and disease estimating parameters along with three checks for disease response in augmented design. All the recommended package of practices was followed along with necessary prophylactic plant protection measures to raise a good crop. Data on different traits and parameters under study were recorded and analysed biometrically to assess the genetic parameter of variability and heritability. The ANOVA showed significant difference among the genotypes for most of the traits and parameters under study which reflects ample amount of variability among the genotypes. Further, the smaller difference between GCV and PCV and higher estimates of heritability and genetic advance as percentage of mean revealed higher percentage of inherent genetic potential in overall variability. The higher estimates of heritability and genetic advance as percent of mean for grain yield per plant and AUDPC suggested that the resistant lines identified in this study can be easily advanced through generation following phenotypic selection for derivation of high yielding resistant lines.

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

QTL analysis for brown spot resistance in American rice cultivar 'Dawn'.

Rice brown spot (BS), caused by Bipolaris oryzae, causes yield loss and deterioration of grain quality. Using single-nucleotide polymorphism (SNP) markers, we conducted quantitative trait locus (QTL) analysis of BS resistance in backcross inbred lines (BILs) from a cross between an American rice cultivar, ‘Dawn’ (resistant), and ‘Koshihikari’ (susceptible). Four QTLs for BS resistance were detected in a three-year field evaluation, and ‘Dawn’ contributed the resistance alleles at all QTLs. The QTL with the greatest effect, qBSR6-kd, explained 15.1% to 20.3% of the total phenotypic variation. Although disease score and days to heading (DTH) were negatively correlated in all three years, qBSR6-kd was located near a QTL for DTH at which the ‘Dawn’ allele promoted heading. Another BS resistance QTL (qBSR3.1-kd) was unlinked to the QTLs for DTH. Therefore, these two QTLs are likely to be useful for breeding BS-resistant varieties without delaying heading. The other two BS resistance QTLs (qBSR3.2-kd and qBSR7-kd) were located near DTH QTLs at which the ‘Dawn’ alleles delayed heading. The QTLs reported here will be good candidates for developing BS-resistant cultivars.

Integration Between Arbuscular Mycorrhizal Fungi, Bacterial and Fungal Bioagents for Controlling Rice Brown Spot Disease

The main purpose of this work was to test the efficiency of Arbuscular mycorrhizal fungi (AMF) in inducing systemic acquired resistance against rice brown spot disease alone or in combinations with the bioagents, Pseudomonas fluorescens (Pf) and Trichoderma viride (T.v.). In vitro test showed inhibition of 52.85% and 57.14%, respectively for the two selected bioagents with deformation and lysis for the pathogen spores. Under greenhouse conditions, the mixture of AMF + Tv and AMF + Pf + Tv, showed the highest efficiency in reducing the disease incidence after the fungicide treatment, whereas they achieved 65.60 and 48.40%, respectively. Field trials at two locations showed potentiality of AMF combined with the other bioagents in decreasing the percentages of disease incidence and severity. Also, the applied treatment increased the plant growth parameters, i.e., chlorophyll content, plant height and yield. Microscopic examination for rice plant roots showed colonization structures of AMF. Also, Pathogenesis related proteins had been defined.

FIELD EVALUATION OF NATIVO 300 SC FUNGICIDE (TRIFLOXYSTROBIN 100 g l-1 + TEBUCONAZOLE 200 g l-1) ON RICE BROWN SPOT (Oryza sativa L.)

Rice brown spot caused by the fungus Bipolaris oryzae is one of the constraints affecting the productivity of rice (Oryza sativa L.) in Côte d'Ivoire. As an effort to find ways of controlling this disease, the efficacy of the fungicide NATIVO 300 SC (trifloxystrobin 100 g l-1 + tebuconazole 200 g l-1) was tested in field conditions at Gbombélo in western Côte d'Ivoire. Five fungicide treatments were studied (water, 2 kg ha-1 of COGA 80 WP (800 g/kg mancozeb), 250 ml ha-1 of NATIVO 300 SC, 300 ml ha-1 of NATIVO 300 SC and 350 ml ha-1 of NATIVO 300 SC), in a Fischer experimental design with four replicates. Results showed a significant effect of fungicides on the severity of brown spot disease, with scores 2 to 3 times lower than in the control plot (water-treated). NATIVO 300 SC at the 350 ml ha-1 dose showed the same severity score as COGA 80 WP, the reference fungicide. Regarding the effect on the yield, NATIVO (300 SC, 350 ml ha-1) performed 9 times better (1.2825 tons ha-1) than the control plot (0.1392 ton ha-1), compared to 6 times for COGA 80 WP (0.9626 ton.ha-1). These results show that NATIVO (300 SC) is as effective, against rice brown spot (Bipolaris oryzae), as COGA 80 WP, the reference fungicide. However, at the dose of 350 ml ha-1, NATIVO 300 SC was the most efficient both from crop protection and agronomic perspective.

First Report of Leaf Spot Disease on Rice Caused by Epicoccum sorghinum in China

Rice is being threatened by many fungal diseases, of which rice blast (RB) and brown leaf spot (BLS) are the two most important rice diseases. Recently, a leaf spot disease similar to RB and BLS was observed on leaf samples collected from Dingyuan, Anhui province. During August to September 2019, 200 plants and 800 leaves of each variety were assessed on seven varieties (Xiushui134, Nanjing46, Jiahe218, Zhongzheyou8, Yongyou12, Yongyou15, and Yongyou1540), and the disease incidence ranged from 9.5 to 69% (plant) and 8.63 to 41.38% (leaf). The symptoms appeared as small chlorotic spots with yellow halos and gray to black centers. Lesions on the older leaves enlarged and coalesced into bigger spots. The infections started from lower leaves and progressed not as rapidly as RB; therefore, lower leaves were badly damaged, whereas flag leaves mostly became slightly infected with several spots. Twenty-one samples were surface sterilized and cultured on water agar at 28°C for 24 h. Among 25 isolations, 19 showed same cultural characters. Morphological analysis was performed following published work (Zeng et al. 2018). The fungal colonies were villose and regular, gray at the center and pale at the margin, and finally turned dark gray, and the back turned scarlet. Chlamydospores, unicellular or multicellular, massively produced. Pycnidia, brown and mostly spheroid, were 67.9 to 117.5 × 44.5 to 90.1 µm, and conidia, unicellular, hyaline, oval, were 3.6 to 5.3 × 1.8 to 2.8 µm. The genes of the fungal pathogen (LSU, ITS, tub2, and rpb2) were amplified, sequenced, and multilocus phylogenetic analyzed according to described procedures (Chen et al. 2015), and the sequences were submitted to GenBank with accession numbers MT125855, MT125854, MT127630, and MN562463, respectively. The multilocus phylogenetic analysis showed the isolated fungus was the closest to Epicoccum sorghinum. In view of both morphological and molecular characteristics, the fungus strains were finally identified as E. sorghinum. The pathogenicity test was repeated three times in both mycelial and conidial inoculation ways. Rice plants at filling stage were inoculated with mycelial plugs and conidial suspension (1 × 10⁶ conidia/ml) separately. In each time, five healthy plants (three for inoculation, two for control) were used. Ten leaves of each plant were wounded and then pinned with a 0.5-cm PDA culture plug or dosed with 6 μl of conidia suspension. The inoculated areas were kept moist with soaked sterilized cotton during the study, and environmental humility was maintained at >80%. After being cultured in the dark for 36 to 48 h at 26°C, these plants were put under a 12-h light/dark cycle. Typical symptoms described in this study appeared on more than 93% of mycelium-inoculated leaves and more than 80% of conidium-inoculated leaves after 7 to 10 days postinoculation. E. sorghinum was consistently reisolated from leaves with symptoms, and hence E. sorghinum was confirmed to cause leaf spot disease on rice. To our knowledge, this is the first report of leaf spot disease on rice caused by E. sorghinum in China. The disease is not expected to cause greater yield losses based on our study. However, the symptoms could be easily mistaken for RB and incur unnecessary chemical sprays.

Expression Analysis of Defense Related Genes in Rice Response to Bipolaris oryzae, the Causal Agent of Rice Brown Spot

The rice defense mechanism was studied against Bipolaris oryzae, the rice brown spot fungus, in two Thai rice varieties, Khao Dawk Mali 105 (KDML 105) and Jao Hom Nin (JHN) (showing highest and lowest susceptibility to B. oryzae, respectively). The expression was evaluated of eight genes through real-time quantitative reverse transcription polymerase chain reaction. The gene involved in the salicylic acid (SA) signaling pathway (OsPAL) and the pathogenesis related genes (OsPR1b and OsPBZ1) were upregulated in both varieties with no significant differences. Despite higher expression of the genes involved in the jasmonic acid (JA) signaling pathway (OsLOX and OsAOS2) in JHN, the expression of JiOsPR10 was not significantly different in both varieties. The genes involved in the ethylene (ET) signaling pathway (OsACS1 and OsEIN2) were expressed more highly and far more rapidly in KDML 105 than JHN. Overall, our results demonstrated that the investigated genes related to SA, JA and ET defense pathways may not play a major role in rice resistance against B. oryzae. Furthermore, the high level of transcript accumulation of genes related to the ET signaling pathway may interfere with the ability of rice to resist B. oryzae. The study provided information for a better understanding of rice defense mechanisms against B. oryzae.

Chemical control of rice brown spot (Bipolaris oryzae) in Paraguay

Chemical control of rice brown spot (Bipolaris oryzae) in Paraguay

リモートセンシングにおいて留意すべきイネごま葉枯病発病程度と正規化植生指数(NDVI)および分光特性との関係

リモートセンシングにおいて留意すべきイネごま葉枯病発病程度と正規化植生指数(NDVI)および分光特性との関係

Studies on the foliar application of Silicon based nutrients against brown spot of rice

Studies on the foliar application of Silicon based nutrients against brown spot of rice

Pathogenic Variability of Bipolaris oryzae Causing Leaf Spot Disease of Rice in West Africa

An assessment of rice crop resistance to Bipolaris oryzae, a causal agent of rice brown leaf spot or helminthosporium disease was carried out. Ten (10) irrigated and upland rice varieties were used in this study. Seven (7) isolates of B. oryzae from Burkina Faso, seven (7) from Côte d’Ivoire and six (6) from Togo were used. The ten (10) varieties were inoculated with B. oryzae strains to assess their effect and determine the resistance level of these varieties to rice leaf spot (Helminthosporium). The varieties Bouaké- AM, FKR60N, NIL130, V10, FKR62N and CY2 showed a good level of resistance against all the isolates. The incidence of the disease on leaves reached 62.22% of severity index. Results revealed also that the strain effects are not linked to their country of origin. The investigation on the pathogenicity of Bipolaris oryzae isolates displayed the existence of a pathogenic variability among the fungus populations. These findings suggest that the tested varieties possess resistance genes that can be used in varietal selection.

Effect of selected fungicides and Bio-Pesticides on the mycelial colony growth of the Helminthosporium oryzae. brown spot of rice

Helminthosporium oryzae, a fungal pathogen causing Brown Spot of Rice is one of the disastrous biotic factors responsible for 16–43% yield in rice. To control in vitro fungal growth of this pathogen, four fungicides viz. Mencozeb, Thiophanate Methyl, Iprovalicarb + Propineb and Propineb were used with four different concentrations: 50, 100, 150 and 200 ppm. Plant extracts of five species as Azadirachta indica, Calotropisprocera, Allium sativum, Datura stramonium and Zingiber officinale were also tested with three doses (5, 10 and 15 ml). While, 24 isolates of Bacterial isolates were investigated for their antagonistic effect. The in vitro results of the four fungicides contributed that Mancozeb and Thiophanate Methyl inhibited the colony growth at the higher doses (150 and 200 ppm) with no linear colony growth. However, Propineb was found moderately effective at (5.00 mm) and Iprovalicarb + Propineb was less effective (7.25 mm). Similarly, the in-vitro efficacy of the five plant extracts against H. oryzae at different doses revealed that Zingiber officinale and Allium sativum were more effective at high doses (00.00 mm, 2. mm). Also, Datura stramonium (4.62 mm) Azadirachta indica (18.00 mm) and Calotropisprocera (12.00 mm) performed better as compared to the control (40.00 mm). The results revealed that out of 24 isolates, 4 isolates of bacteria Agrobacterium spp, Xanthomonas, Erwinia, Streptomyces has inhibited the linear colony growth of H. oryzae up to 1-2 mm. This study can be useful for controlling the fungal disease as the two fungicides Mancozeb and Thiophanate Methyl were more effective and the Zingiber officinale and Allium sativum plant extracts were highly effective against the brown spot of rice. Biocontrol also reduced leaner colony Helminthosporium oryzae.

Rice defense responses to Bipolaris oryzae mediated by a strobilurin fungicide

Strobilurins, also known as quinone outside inhibitors (QoIs), are one of the most important fungicide groups for controlling plant diseases. In addition, they can prime plants for enhanced defense against pathogen attack. Here, detailed biochemical and microscopic analyses were performed to investigate whether azoxystrobin (Az) could enhance defense responses to Bipolaris oryzae, the cause of brown spot of rice. Fungal infection increased the activity of all defense enzymes regardless of fungicide treatment. Az-sprayed plants displayed higher activities of β-1,3-glucanase, peroxidase, polyphenol oxidase (PPO) and lipoxygenase (LOX) in the absence of B. oryzae inoculation and of phenylalanine ammonia-lyase, PPO and LOX at 24 h after inoculation (hai) with B. oryzae compared to the control plants. Concentrations of total soluble phenols at 144 hai were higher in the Az-sprayed plants relative to their non-treated counterparts. Concentrations of lignin-thioglycolic acid derivatives were increased following fungal infection in the control plants. Hyphae from B. oryzae colonized bulliform, bundle sheath, epidermal, guard, mesophyll and vascular bundle cells besides intercellular spaces, but fewer and smaller fungal cells were noticed in the Az-sprayed than in the control plants. Therefore, although Az transiently reprogrammed activities of some defense enzymes, its fungicidal activity appears to have played a major role in reducing B. oryzae infection.

In vitro Management of Bipolaris oryzae the Causal Pathogen of Brown Spot of Rice by Plant Extracts

In vitro Management of Bipolaris oryzae the Causal Pathogen of Brown Spot of Rice by Plant Extracts