International Center for Research and Education in Agriculture, Nagoya University, Chikusa, Nagoya 464-8601, Japan.
Published: December 2019
AI Article Synopsis
Article Abstract
A total of 99 isolates of rice blast ( Cavara) were collected from 2010 to 2015 from four regions in Kenya: Kirinyaga County and Embu County, Kisumu County, Tana River County, and Mombasa County. The pathogenicities of these isolates were clarified based on the reaction patterns of Lijiangxintuanheigu and differential varieties (DVs) targeting 23 resistance genes. The frequency of virulent isolates was high for DVs for , , , , (t), , , , , , , (t), (t), and (t); low for DVs for , (t), , and ; and intermediate for the remaining DVs for , , , , and (t). These blast isolates were classified into three cluster groups: Ia, Ib, and II. The frequencies of virulent isolates to DVs for , , , , , , , (t), , and (t) differed markedly between clusters I and II, and those of DVs for , , , , , , and (t) differed between Ia and Ib. The frequencies of cluster groups in the four geographical regions were different. A total of 62 races were found, with 19 blast isolates categorized into one race (U63-i7-k177-z00-ta003), whereas the other races included only some isolates in each.
The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert's experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases.
The rapid virulence variation of () to rice is a big challenge for rice blast control. Even though many studies have been done by scientists all over the world, the mechanism of virulence variation in remains elusive. AM16, an avirulent strain reported in our previous study, provides an excellent entry point to explore the mechanism of virulence variation in .
Blast disease caused by is a devastating disease that limits rice grain production. Here, we synthesized rhamnolipid (RL) modified silica nanoparticles (SiONPs) based on the excellent antimicrobial activity of RL against various phytopathogens and the role of SiONPs in alleviating plant diseases and investigated the roles and mechanisms of RL@SiONPs application in controlling rice blast disease. Two-week-old rice seedlings were sprayed with 100 mL/L of different materials before pathogen inoculation, and blast incidence was investigated 5 days after inoculation.
Background: Rice blast caused by Pyricularia oryzae is a major threat to rice production worldwide. Tainung 84 (TNG84) is an elite japonica rice cultivar developed through the traditional pedigree method. It has maintained superior blast resistance since its release in 2010.
causes devastating rice blast disease, significantly impacting rice production in many countries. Among the many known resistance (R) genes, confers broad-spectrum resistance to isolates and encodes a nucleotide-binding site leucine-rich repeat receptor (NLR). Although Piz-t-interacting proteins and those in the signal transduction pathway have been identified over the last decade, the Piz-t-mediated resistance has not been fully understood at the transcriptomic and metabolomic levels.
