土壤2024，Vol.56Issue(4)：879-888,10.DOI:10.13758/j.cnki.tr.2024.04.023
基于机器学习和土壤关键要素的烤烟品质数字制图——以云南玉溪烟区为例
Digital Mapping of Flue-Cured Tobacco Quality Based on Machine Learning and Soil Key Elements—A Case Study of Yuxi Tobacco Area in Yunnan Province,China
摘要
Abstract
In this study,Yuxi City of Yunnan Province,a typical tobacco-planting area in China,was selected as the study object,based on a dataset consisting of 156 pairs of soil-tobacco quality grades,soil key elements were identified through principal component analysis,and then three machine learning methods,namely the Back Propagation Neural Network(BPNN),Random Forest(RF)and Support Vector Machine(SVM)were employed to construct the prediction model of tobacco quality grade in order to achieve its spatial prediction and mapping.The results showed that based on 17 soil indicators,11 specific indicators were identified as soil key elements,among these,clay content exhibited the highest contribution(accounting for 18.5％)to the variation in tobacco quality grades.The independent validation demonstrated that RF model achieved the highest accuracy(0.78)and Kappa coefficient(0.76)in the predictive performance,followed by SVM model,while BPNN model exhibited the least favorable outcomes.In terms of recall and precision,RF model demonstrated a descending level of accuracy in correctly categorizing tobacco quality grades,with the order of Level 5＞Level 1＞Level 2.Tobacco quality of Level 1 and 5 were predominantly distributed in the eastern part of Yuxi,with the easternmost Huanning County being the prime cultivation area for high-quality tobacco.关键词
烤烟品质/机器学习/土壤关键要素/空间分布/制图Key words
Tobacco quality/Machine learning/Soil key elements/Spatial distribution/Mapping分类
农业科技引用本文复制引用
陶怡,王美艳,史学正,孙维侠,王世航,李湘伟,朱云聪,谢新乔..基于机器学习和土壤关键要素的烤烟品质数字制图——以云南玉溪烟区为例[J].土壤,2024,56(4):879-888,10.基金项目
红塔烟草(集团)有限责任公司科技项目(KY-Y60023015)资助. （集团）
