Prediction and visualization of moisture content in Tencha drying processes by computer vision and deep learning

Abstract

BACKGROUNDMonitoring and controlling the moisture content throughout the Tencha drying processing procedure is crucial for ensuring its quality. Workers often rely on their senses to perceive the moisture content, leading to relative subjectivity and low reproducibility. The traditional drying methods for measuring moisture content is destructive to samples. This research was conducted using computer vision combined with deep learning for detecting moisture content during the Tencha drying process. Different color space components of Tencha drying samples’ image were first extracted by computer vision. The color components were preprocessed using MinMax and Z‐score. Subsequently, one‐dimensional convolutional neural network (1D‐CNN), partial least squares, and backpropagation artificial neural network models were built and compared.RESULTSThe 1D‐CNN model and Z‐score preprocessing achieved superior predictive accuracy, with correlation coefficient of prediction (Rp) = 0.9548 for moisture content. Furthermore, the migration of moisture content during the Tencha drying process was eventually visualized by mapping its spatial and temporal distributions.CONCLUSIONThe results indicated computer vision combined with 1D‐CNN was feasible for moisture prediction during Tencha drying process. This study provides technical support for the industrial and intelligent production of Tencha.This article is protected by copyright. All rights reserved.