scholarly journals Remote Sensing of Occurrence Place of Rice Blast Disease by Infrared Thermal Image.

1995 ◽  
Vol 64 (3) ◽  
pp. 467-474 ◽  
Author(s):  
Haruhiko YAMAMOTO ◽  
Yoshinori SUZUKI ◽  
Masanori IWANO ◽  
Seiji HAYAKAWA
Keyword(s):  
Remote Sensing ◽  
Rice Blast ◽  
Thermal Image ◽  
Rice Blast Disease ◽  
Blast Disease

scholarly journals Identificação de progênies de arroz resistentes à brusone no Estado de São Paulo, em 1976/77

Bragantia ◽  
1985 ◽  
Vol 44 (1) ◽  
pp. 311-329
Author(s):  
Jaciro Soave ◽  
Luiz Ernesto Azzini ◽  
Octávio Bento de Almeida Camargo ◽  
Armando Pettinelli Júnior ◽  
Mauro Sakai
Keyword(s):  
Rice Blast ◽  
Sao Paulo ◽  
Pyricularia Oryzae ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
São Paulo

Este trabalho apresenta os resultados das pesquisas realizadas para a avaliação da resistência à brusone (Pyricularia oryzae Cav.) dos principais materiais de sequeiro e irrigado do programa de melhoramento genético do Instituto Agronômico do Estado de São Paulo, e de genótipos exóticos, introduzidos de diversos países, visando à obtenção de cultivares de arroz resistentes àquela limitante doença fúngica. Os testes foram realizados em condições de campo, em canteiros padronizados para reação uniforme a P. oryzae, e a avaliação das plantas foi feita através da observação dos sintomas visuais deixados pela doença, aos quais foram atribuídas notas de 1 a 7, conforme a escala de notas adotadas no "Symposium on the rice blast disease", em 1963. Sessenta e três germoplasmas de arroz de sequeiro e trinta de cultivo irrigado foram testados quanto à resistência à brusone na folha, nas seguintes localidades paulistas: Itararé, Mococa, Pariquera-Açu, Pindamonhangaba, Pindorama e Ribeirão Preto. Foram ainda avaliados 102 genótipos exóticos de arroz visando à detecção de fontes de resistência à brusone nas mesmas localidades, além de Campinas. Somente cinco cultivares de sequeiro, GS-73-164, GS-73-165, GS-73-94, IAC-25 e GS-73-17, e dois cultivares de arroz irrigado, IAC-120 e Pinda F-3-7, embora suscetíveis, apresentaram comportamento satisfatório quanto à brusone. Dos genótipos exóticos testados, vinte e sete foram indicados como fontes de resistência à brusone no Estado de São Paulo.

scholarly journals Role of two metacaspases in development and pathogenicity of the Rice Blast fungus, Magnaporthe oryzae

2020 ◽  
Author(s):  
Jessie Fernandez ◽  
Victor Lopez ◽  
Lisa Kinch ◽  
Mariel A. Pfeifer ◽  
Hillery Gray ◽  
...  
Keyword(s):  
Cell Death ◽  
Food Security ◽  
Life Cycle ◽  
Magnaporthe Oryzae ◽  
Rice Blast ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Complex Life Cycle ◽  
Insight Into

ABSTRACTRice blast disease caused by Magnaporthe oryzae is a devastating disease of cultivated rice worldwide. Infections by this fungus lead to a significant reduction in rice yields and threats to food security. To gain better insight into growth and cell death in M. oryzae during infection, we characterized two predicted M. oryzae metacaspase proteins, MoMca1 and MoMca2. These proteins appear to be functionally redundant and are able to complement the yeast Yca1 homologue. Biochemical analysis revealed that M. oryzae metacaspases exhibited Ca2+ dependent caspase activity in vitro. Deletion of both MoMca1 and MoMca2 in M. oryzae resulted in reduced sporulation, delay in conidial germination and attenuation of disease severity. In addition, the double ΔMomca1mca2 mutant strain showed increased radial growth in the presence of oxidative stress. Interestingly, the ΔMomca1mca2 strain showed an increase accumulation of insoluble aggregates compared to the wild-type strain during vegetative growth. Our findings suggest that MoMca1 and MoMca2 promote the clearance of insoluble aggregates in M. oryzae, demonstrating the important role these metacaspases have in fungal protein homeostasis. Furthermore, these metacaspase proteins may play additional roles, like in regulating stress responses, that would help maintain the fitness of fungal cells required for host infection.IMPORTANCEMagnaporthe oryzae causes rice blast disease that threatens global food security by resulting in the severe loss of rice production every year. A tightly regulated life cycle allows M. oryzae to disarm the host plant immune system during its biotrophic stage before triggering plant cell death in its necrotrophic stage. The ways M. oryzae navigates its complex life cycle remains unclear. This work characterizes two metacaspase proteins with peptidase activity in M. oryzae that are shown to be involved in the regulation of fungal growth and development prior to infection by potentially helping maintain fungal fitness. This study provides new insight into the role of metacaspase proteins in filamentous fungi by illustrating the delays in M. oryzae morphogenesis in the absence of these proteins. Understanding the mechanisms by which M. oryzae morphology and development promote its devastating pathogenicity may lead to the emergence of proper methods for disease control.

scholarly journals Diagnosis of Rice Blast Disease in Different Environmental Conditions using Image Processing Technique

2020 ◽  
Vol 17 (100) ◽  
pp. 17-28
Author(s):  
Ezzatollah Askari Asli Ardeh ◽  
mohammad reza larijani ◽  
Reyhaneh Loni ◽  
◽  
◽  
...  
Keyword(s):  
Image Processing ◽  
Environmental Conditions ◽  
Rice Blast ◽  
Processing Technique ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Image Processing Technique

Microconidia: Understanding Its Role in the Fungus Magnaporthe oryzae Inciting Rice Blast Disease

2021 ◽  
pp. 143-150
Author(s):  
Ganesan Prakash ◽  
Asharani Patel ◽  
Ish Prakash ◽  
Kuleshwar Prasad Sahu ◽  
Rajashekara Hosahatti ◽  
...  
Keyword(s):  
Magnaporthe Oryzae ◽  
Rice Blast ◽  
Rice Blast Disease ◽  
Blast Disease

Investigating the biology of rice blast disease and prospects for durable resistance

2021 ◽  
pp. 643-680
Author(s):  
Vincent M. Were ◽  
◽  
Nicholas J. Talbot ◽  
Keyword(s):  
Cell Biology ◽  
Rice Blast ◽  
Plant Pathogens ◽  
Blast Resistance ◽  
Durable Resistance ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Global Food Security ◽  
Major Resistance Genes ◽  
Effector Secretion

There are important biological process involved in rice blast disease that are now well-studied during the early events in plant infection which include: the cell biology of appressorium formation, the biology of invasive growth and effector secretion, the two distinct mechanisms of effector secretion, the nature of the plant-pathogen interface, PAMP-triggered immunity modulation by secreted effectors and effector-triggered immunity and blast resistance. The devastating losses caused by the blast fungus have been documented in most grasses, but this chapter discusses the use of major resistance genes to rice blast and wheat blast disease as an emerging threat to global food security. This chapter also highlights an emerging approach to breed for durable resistance to plant pathogens using gene editing technologies with an example: CRISPR-Cas9 mutagenesis of dominant S-genes for disease control.

Pyricularia Grisea the Correct Name for the Rice Blast Disease Fungus

Mycologia ◽  
1990 ◽  
Vol 82 (4) ◽  
pp. 509-512 ◽  
Author(s):  
Amy Y. Rossman ◽  
Richard J. Howard ◽  
Barbara Valent
Keyword(s):  
Rice Blast ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Pyricularia Grisea

scholarly journals Predicting rice blast disease: machine learning versus process-based models

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
David F. Nettleton ◽  
Dimitrios Katsantonis ◽  
Argyris Kalaitzidis ◽  
Natasa Sarafijanovic-Djukic ◽  
Pau Puigdollers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rice Blast ◽  
Machine Learning Algorithms ◽  
Rice Blast Disease ◽  
Blast Disease ◽  
Data Driven ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Plant Disease Management ◽  
Process Based Models

Abstract Background In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and Water Accounting Rice Model (WARM)) and two approaches based on machine learning algorithms (M5Rules and Recurrent Neural Networks (RNN)), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared. Results Results clearly showed that the models succeeded in providing a warning of rice blast onset and presence, thus representing suitable solutions for preventive remedial actions targeting the mitigation of yield losses and the reduction of fungicide use. All methods gave significant “signals” during the “early warning” period, with a similar level of performance. M5Rules and WARM gave the maximum average normalized scores of 0.80 and 0.77, respectively, whereas Yoshino gave the best score for one site (Kalochori 2015). The best average values of r and r2 and %MAE (Mean Absolute Error) for the machine learning models were 0.70, 0.50 and 0.75, respectively and for the process-based models the corresponding values were 0.59, 0.40 and 0.82. Thus it has been found that the ML models are competitive with the process-based models. This result has relevant implications for the operational use of the models, since most of the available studies are limited to the analysis of the relationship between the model outputs and the incidence of rice blast. Results also showed that machine learning methods approximated the performances of two process-based models used for years in operational contexts. Conclusions Process-based and data-driven models can be used to provide early warnings to anticipate rice blast and detect its presence, thus supporting fungicide applications. Data-driven models derived from machine learning methods are a viable alternative to process-based approaches and – in cases when training datasets are available – offer a potentially greater adaptability to new contexts.

Sign in / Sign up

Export Citation Format

Share Document