Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles
نویسندگان
چکیده
منابع مشابه
Bioefficacy of Strobilurin Based Fungicides against Rice Sheath Blight Disease
As the usage of fungicides catapulted with the onward march of the dial hour, fungicidal resistance by the pathogens emerged as a new constraint. This amalgamated with the growing demand by the farmers for crop protection agents with low use rates, a benign environmental profile and a low toxicity to human and wild life, further gave an impetus to the search of new molecule of fungicides with n...
متن کاملSheath Blight and Its Management in Rice
Rhizoctonia solani AG1-IA, is the most economically important disease in Texas and other southern rice-producing states. The disease is widespread and occurs every year. The disease was first reported in Japan in 1910, and was soon well established in many Asian countries (Lee and Rush, 1983). The pathogen is capable of infecting rice, soybeans, grass weeds, and hundreds of other plant species ...
متن کاملDetection of rice sheath blight for in-season disease management using multispectral remote sensing
Timely diagnosis of crop diseases in fields is critical for precision on-farm disease management. Remote sensing technology can be used as an effective and inexpensive method to identify diseased plants in a field scale. However, due to the diversity of crops and their associated diseases, application of the technology to agriculture is still in research stage, which needs to be elaborately inv...
متن کاملMapping and validation of QTLs for rice sheath blight resistance
Sheath blight, caused by Rhizoctonia solani, is one of the most serious diseases of rice. Among 33 rice accessions, mainly from National Institute of Agrobiological Sciences (NIAS) Core Collection, we found three landraces from the Himalayas-Jarjan, Nepal 555 and Nepal 8-with resistance to sheath blight in 3 years' field testing. Backcrossed inbred lines (BILs) derived from a cross between Jarj...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Plant Phenomics
سال: 2020
ISSN: 2643-6515
DOI: 10.34133/2020/8954085