8 Jun 2026

MoistViT: A Vision Transformer Model for Moisture Content Prediction of Wood Chips

Abstract: Moisture content in wood chips is a critical parameter for industries such as pelleting mills, bio-refineries, paper mills, and renewable energy production. The moisture level significantly influences both the quality of the final product and the efficiency of the production process. Consequently, accurate knowledge of moisture content is of substantial importance to wood chip-reliant industries. However, current methods for determining moisture content are either time-consuming or require costly equipment and specialized setups. Therefore, developing a quick and reliable method for assessing wood chip moisture content is imperative. To address this need, we evaluate fourteen Vision Transformer (ViT) architectures and introduce an optimized model, MoistViT, developed using Bayesian Optimization Hyperband (BOHB) for efficient hyperparameter tuning. Experiments on two wood chip image datasets (1600 total images) show that MoistViT achieves 91% accuracy and 92% F1-score on Source 1 and 93% accuracy and 93% F1-score on Source 2, outperforming all baseline models. Subsequently, a thorough analysis of failure cases has been carried out, including the identification of the most challenging groups of moisture levels. These analyses provide valuable insights into the complex task of determining moisture content from inherently heterogeneous wood chips. The proposed MoistViT demonstrates significant potential for real-time applications in relevant industries, which could ultimately lead to a streamlined production process.

16 Feb 2024

A Comprehensive Review on Wood Chip Moisture Content Assessment and Prediction

Abstract: Wood chips are the primary sources of raw materials for numerous industries, including pelleting mills, biorefineries, pulp-and-paper industries, and biomass-based power generation facilities. Unfortunately, when wood chips are utilized as a renewable and environmentally friendly resource, industries are constantly challenged by the consistency of the wood chip qualities (e.g., moisture/ash contents, size distributions) - a historically recognized problem on a global scale. Among other wood chip quality attributes, the moisture content is considered the most pressing one as it directly impacts the energy content, storage stability, and handling properties of the raw and finished products. Therefore, accurate wood chip moisture content prediction can help optimize the drying process and reduce energy consumption. In this review, a survey was conducted on various techniques and models employed for predicting wood chip moisture content. The advantages and limitations of these approaches, as well as their potential applications and future directions were also discussed. This review aims to provide a comprehensive overview of the current state-of-the-art in wood chip moisture content prediction and to highlight the challenges and opportunities for further research and development in this field.