Moisture content prediction of cigar leaves air-curing process based on stacking ensemble learning model.

ABSTRACT:

Introduction

Accurately determining the moisture content of cigar leaves during the air-curing process is crucial for quality preservation. Traditional measurement techniques are often subjective and destructive, limiting their practical application.

Methods

In this study, we propose a stacking ensemble learning model for non-destructive moisture prediction, leveraging image-based analysis of naturally suspended cigar leaves. In this study, front and rear surface images of cigar leaves were collected throughout the air-curing process. Color and texture features were extracted from these images, and a filtering method was applied to remove redundant variables. To ensure optimal model selection, the entropy weight method was employed to comprehensively evaluate candidate machi

SUBMITTER: Xing Z 

PROVIDER: S-EPMC11979227 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature