首页|Researchers from Beijing Academy of Agricultural and Forestry Sciences Describe Findings in Machine Learning (Developing a Comprehensive Evaluation Model of Variety Adaptability Based On Machine Learning Method)
Researchers from Beijing Academy of Agricultural and Forestry Sciences Describe Findings in Machine Learning (Developing a Comprehensive Evaluation Model of Variety Adaptability Based On Machine Learning Method)
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NETL
NSTL
Elsevier
New research on Machine Learning is the subject of a report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Context or problem: A comprehensive evaluation of the adaptability of maize varieties is very important to accurately promote new varieties and reduce the risk of using them. However, challenges are faced when accurately promoting new varieties owing to the lack of a comprehensive model to evaluate the adaptability of a variety for a specific area, such as a district or county.Objective or research question: The purpose of this study was to construct a new maize (Zea mays) Variety Adaptability Comprehensive Evaluation Index (VACEI) by combining nine agronomic traits; including the lodging rate, stalk rot (Fusarium graminearum) resistance, ear rot (Fusarium graminearum) resistance, Curvularia leaf spot (Curvularia lunata) resistance, southern leaf blight (Bipolaris maydis) resistance, common smut (Ustilago maydis) resistance, southern corn rust (Puccinia polysora) resistance, growth period, and yield; that utilized a machine learning method to construct a prediction model for the VACE and estimate the adaptability of maize varieties on a district or county scale.” Financial support for this research came from Sci-Tech Innovation 2030 Agenda.
BeijingPeople’s Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningBeijing Academy of Agricultural and Forestry Sciences
NETL
NSTL
Elsevier
