中国烟草科学2025，Vol.46Issue(2)：113-120,8.DOI:10.13496/j.issn.1007-5119.2025.02.014
基于集成机器学习的大理红花大金元种植适宜性区划
Planting Suitability Zones of Honghuadajinyuan in Dali Prefecture Based on Integrated Machine Learning
摘要
Abstract
The flue-cured tobacco variety"Honghuadajinyuan"exhibits poor ecological adaptability.To establish an intelligent zoning method for its cultivation suitability in Dali tobacco-growing area,seven ecological environmental covariates were selected,including altitude,precipitation during the maturity period,mean temperature during the maturity period,hydrothermal coefficient,soil pH,available potassium content,and water-soluble chlorine content.Using 752 survey datasets as training samples and the ensemble of machine learning algorithms,we investigated the cultivation suitability zoning of"Honghuadajinyuan"in Dali Prefecture.Results demonstrated as the follows.(1)The suitability of"Honghuadajinyuan"shows a multidimensional nonlinear relationship with environmental covariates,validating the use of nonlinear machine learning models.(2)The optimal models were CHAID decision trees integrated with bagging and boosting algorithms.In the suitability class model,the hydrothermal coefficient,soil available potassium,and maturity-period precipitation were the most critical indicators;In the suitability level model,maturity-period precipitation,mean temperature during the maturity period,and altitude were prioritized.(3)Among 1 458 evaluation units,471 were classified as"most suitable",456 as"moderately suitable"and 531 as"unsuitable".Suitable cultivation areas were concentrated in Jianchuan,Yunlong,Eryuan,Dali Prefecture,Weishan,Midu,and Nanjian counties,as well as the eastern and western parts of Binchuan and the southwestern region of Yongping.(4)Validation using sensory evaluation data from 48 tobacco leaf samples confirmed the alignment of zoning results with actual quality.These findings provide a scientific basis for optimizing"Honghuadajinyuan"cultivation zoning in Dali Prefecture.关键词
机器学习/红花大金元/种植适宜性评价/大理州Key words
machine learning/Honghuadajinyuan/planting suitability assessment/Dali prefecture分类
农业科学引用本文复制引用
翁倩文,陈伟强,王德勋,陈逸林,史宏志,马月红,于滢,苑钰珂..基于集成机器学习的大理红花大金元种植适宜性区划[J].中国烟草科学,2025,46(2):113-120,8.基金项目
中国烟草总公司云南省公司科技项目(30802431) （30802431）
