数字农科院2.0

Machine Learning-Based Prediction of Flue-Cured Tobacco Quality Using Chemical Composition Analysis: A Case Study in Sichuan Province, China

文献类型： 外文期刊

作者： Jun Qiu;Rui Bie;Zhiying Wang;Jingxian Sun;Xiaojie Li;Dongmei Jin;Jianmin Cao

作者机构：

关键词： Machine learning;Quality evaluation;Random forest;SHAP value;Sichuan tobacco

期刊名称： Advances in Transdisciplinary Engineering

ISSN： 2352-751X

年卷期： 2025 年 66 卷

页码：

收录情况： EI（2025版）

摘要： Background: In this study, we aimed to establish an efficient and accurate machine -learning model for evaluating tobacco quality based on its chemical composition. To achieve this, we gathered a dataset comprising 188 tobacco samples taken from the production area of Sichuan, China in 2021. Four machine learning algorithms—Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting Classifier (GBC)—were used to establish a predictive model for assessing tobacco quality. The study focused on comparing the predictive performance of these models and exploring the upper limit of prediction accuracy using genetic algorithm (GA) hyperparameter optimization. Additionally, the SHAP value model interpretation framework was introduced to provide a comprehensive global interpretation and conduct feature dependency analysis. Results: The results showed that model accuracy ranked as RF>GBC> KNN>SVM, with the GA-RF machine learning model achieving a prediction accuracy of 86.8%. SHAP values identified seven important characteristic indexes affecting Sichuan tobacco leaf quality, highlighting RF as the optimal classifier for predicting flue-cured tobacco quality in Sichuan Province. Conclusions: The GA-RF model constructed in this study effectively identifies Sichuan tobacco leaf quality. These findings offer novel insights and data support for the application of machine learning algorithms in tobacco fields and tobacco leaf quality evaluation.

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