Quantifying the influencing factors and predictive analysis of cotton defoliation and maturation based on machine learning

作者：Yukun Wang, Chenyu Xiao, Kexin Li, Meng Lu, Xinghu Song, Haikun Qi, Yao Wang, Zhenwang Zhang, Xinghua Yu, Fangjun Li, Sumei Wan, Guodong Chen, Dongyong Xu, Xin Du, Mingwei Du, Xiaoli Tian, Zhaohu Li · 发表于：Computers and Electronics in Agriculture · 年份：2025 · DOI：10.1016/j.compag.2025.110555 · 被引用次数：4 · 研究领域：Research in Cotton Cultivation、Smart Agriculture and AI、Remote Sensing in Agriculture

Good defoliation and boll opening are essential for cotton mechanical harvesting. However, in actual production, many factors can affect the effect of defoliation and boll opening rate. Assessing the importance of factors affecting cotton defoliation and ripening, and predicting these factors, is critical to adjust the key influencing factors for achieving optimal defoliation and boll opening effect. This study, conducted from 2016 to 2022 across the three major Chinese cotton-producing regions – the Yellow River Valley, the Yangtze River Valley, and the Xinjiang Autonomous Region – aimed to evaluate the key factors influencing post-application defoliation and ripening processes and to develop models predicting defoliation and ripening progression using three machine learning methods: Random Forest, Support Vector Machines, and Gradient Boosting Machine. The results show that machine learning-selected variables accurately predict defoliation percentage (DP), boll opening percentage (BOP), and the increment of boll opening percentage (IBOP). Crucially for DP, considering the dosage of applied XSL, as well as other defoliants, is essential. Also important are the highest temperatures’ influence and pre-treatment boll opening percentage on defoliation in the Yellow River Valley and Xinjiang Autonomous Region. For BOP, enhancing the pre-treatment boll opening percentage is vital across all regions. Sunshine, humidity, and precipitation levels, especially in the Yellow River, Yang...