Blast Disease of Rice: Evolution and Adaptation in Context of Changing Climate

Singh, Rashmi; Maurya, Sudarshan

doi:10.1007/978-3-030-60585-8_8

Cited by 2 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the change in climate has boosted the populations of pests and disease‐causing microbes, thus limiting the crop's tolerance to biotic stresses (Shahzad et al., 2021). Therefore, the crop's increased susceptibility to diseases and insect damage leads to severe yield loss in cereal crops (Singh & Maurya, 2021). Poor agricultural practices and irrigation systems can also lead to yield declines (Huang et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for major food crops breeding: Applications, challenges, and ways forward

et al. 2023

View full text Add to dashboard Cite

Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning for major food crops breeding: Applications, challenges, and ways forward

et al. 2023

View full text Add to dashboard Cite

show abstract

Prevalence and distribution of rice blast disease in different rice ecosystems of Karnataka, India

Chittaragi

Naik

Pramesh

et al. 2022

Oryza

View full text Add to dashboard Cite

Rice is the most widely consumed cereal staple food for a significant part of the world, particularly in Asia. The Karnataka state of India is one of the highest rice producers, and it has a varied rice ecosystem from irrigated plains to rainfed hilly areas. The rice blast occurs at different severity in these ecosystems causing significant losses each year. The roving survey was carried out in the 120 villages of 18 districts distributed under five irrigated and two rainfed ecosystems of Karnataka during Kharif -2019. Within the irrigated ecosystems, the highest PDI was observed in the Kavery (50.85), followed by Varada (45.89), Bhadra (45.82), Tungabhadra (11.13), and Upper Krishna (10.58) command areas. In a rainfed ecosystem, the highest PDI was observed in the hilly ecosystem (53.38) and the least in the coastal ecosystem (3.73). Within 18 districts, the lowest PDI was observed in the Gadag district (1.68) of the Thungabhadra ecosystem, and the highest was observed in the Chikkamagalur district (81.60) of the hilly ecosystem. The disease was severe in the rainfed hilly ecosystem, followed by an irrigated and rainfed coastal ecosystem. This information is helpful in formulating the management strategies of rice blast in different rice ecosystems of Karnataka.

show abstract

Blast Disease of Rice: Evolution and Adaptation in Context of Changing Climate

Cited by 2 publications

References 30 publications

Machine learning for major food crops breeding: Applications, challenges, and ways forward

Machine learning for major food crops breeding: Applications, challenges, and ways forward

Prevalence and distribution of rice blast disease in different rice ecosystems of Karnataka, India

Contact Info

Product

Resources

About