4 research outputs found
Efficacy of Natural Plant Products on the Control of Aggregate Sheath Spot of Rice
No full text Aqueous extracts from ginger, pepper, basil, and garlic plants and essential oils from neem, garlic, lemongrass, and cinnamon were evaluated for their antagonistic effects against Rhizoctonia oryzae-sativae, the cause of aggregate sheath spot of rice. The compounds in 5% concentrations in water or agar were tested on several R. oryzae-sativae isolates. Cinnamon oil, the most efficacious plant product in vitro, was further tested in the greenhouse for the control of the disease on two rice cultivars inoculated with R. oryzae-sativae. One milliliter of each of four cinnamon oil concentrations (12.5, 37.5, 62.5, or 87.5%) diluted in vegetable oil was applied to the surface of the water in constantly flooded pots. Cinnamon oil failed to reduce the disease caused by one of the isolates at any concentration. Cinnamon oil suppressed the disease caused by the other isolate on one of the cultivars at a concentration of 37.5%, and on both cultivars at a concentration of 62.5 and 87.5%. However, cinnamon oil at 87.5% was phytotoxic. Cinnamon oil has potential to control aggregate sheath spot but relatively high concentrations were required for disease suppression. </jats:p
Genetic Diversity and Aggressiveness of Bipolaris oryzae in North-Central Thailand
Get PDFOne hundred and ten isolates of Bipolaris oryzae, the causal agent of rice brown spot disease were collected from paddy fields in four provinces of north-central Thailand, including Ang Thong, Chai Nat, Lop Buri, and Sing Buri. DNA polymorphism of some Bipolaris oryzae isolates was determined by VNTR, ISSR and RAPD markers. Only VNTR-MR primer showed different fingerprint patterns among these isolates, therefore this primer was selected to study genetic diversity of the Bipolaris oryzae population. In total, there were three haplotypes corresponding to the results from cluster analysis; each of the three clusters shared identical haplotype. The majority of the isolates were separated into group A (88.18%), indicating predominant asexual reproduction of clonal population. However, there was no relationship between haplotype and either collection provinces or aggressiveness on rice. Among four rice varieties tested, including Khao Dawk Mali 105 (KDML 105), RD31, Pathum Thani 1, and Jao Hom Nin (JHN), JHN was the most resistant variety, while KDML 105 was the most susceptible to brown spot disease
Expression Analysis of Defense Related Genes in Rice Response to Bipolaris oryzae, the Causal Agent of Rice Brown Spot
Get PDFThe 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
การประยุกต์ใช้ปัญญาประดิษฐ์ในการจัดการโรคพืชยุคใหม่: โอกาสที่มาพร้อมข้อควรระวังThe Application of Artificial Intelligence (AI) in Modern Plant Disease Management: Opportunities with Cautionary Considerations
Get PDFโรคพืชเป็นปัญหาสำคัญต่อการเกษตรทั่วโลก ส่งผลกระทบต่อผลผลิตพืชและสร้างความเสียหายทางเศรษฐกิจ ซึ่งทำให้ปัญญาประดิษฐ์ Artincial Intelligence (Al) โดยเฉพาะ Machine Leaming (ML) และ Deep Leaming (DL) 1A กลายเป็นเครื่องมือที่มีประสิทธิภาพสำหรับการตรวจหาโรคพืชในช่วงเริ่มต้น การวินิจฉัยโรค การพยากรณ์โรค และการจัดการโรคที่เกิดขึ้น [1], [2]โดย AI สามารถวิเคราะห์ภาพถ่ายข้อมูลสภาพแวดล้อม และทำนายการระบาดของโรคพืชได้ซึ่งเทคโนโลยีนี้ช่วยเสริมสร้างความยั่งยืนในด้านเกษตรรมและเพิ่มประสิทธิภาพในการป้องกันโรคตั้งแต่เริ่มต้นก่อนที่จะแพร่กระจาย และช่วยลดความเสียหายของผลผลิต [2
