Application·Reflection
Lei Lei, Chen Ang, Zhang Shuai, Zhou Xuan, Guo Xiaoxue
2026, 605(2): 106-108.
This paper reviews the data stream collection and application in the process from cotton harvesting to ginning in the United States, and establishes a prediction model aimed at quantifying the impact of fiber quality and seed cotton variety characteristics on ginning rate by standardizing and integrating multi-source heterogeneous data, such as cotton bale identification, fiber quality, and agronomic traits. Studies have shown that cotton fiber quality attributes, such as impurity content, micronaire value, reflectance, and yellowness; and seed cotton variety characteristics, including cotton fiber percentage, bract hair, moisture content, and fiber density, are all key factors affecting the ginning rate. This paper systematically summarizes the relevant data stream architecture, the mechanism of action of key influencing factors, and modeling methods, with a view to providing references for the intelligent upgrading and processing efficiency optimization of China's cotton industry.