MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement

TL;DR

This paper introduces MoistNet, a machine vision-based deep learning approach for rapid, accurate, and portable moisture content measurement of wood chips, outperforming traditional methods and existing models.

Contribution

The study develops two novel neural networks, MoistNetLite and MoistNetMax, using Neural Architecture Search for improved accuracy and efficiency in moisture content prediction from RGB images.

Findings

MoistNetLite achieves 87% accuracy with low computational cost.

MoistNetMax achieves 91% accuracy with high precision.

Models outperform existing deep learning approaches in speed and accuracy.

Abstract

Quick and reliable measurement of wood chip moisture content is an everlasting problem for numerous forest-reliant industries such as biofuel, pulp and paper, and bio-refineries. Moisture content is a critical attribute of wood chips due to its direct relationship with the final product quality. Conventional techniques for determining moisture content, such as oven-drying, possess some drawbacks in terms of their time-consuming nature, potential sample damage, and lack of real-time feasibility. Furthermore, alternative techniques, including NIR spectroscopy, electrical capacitance, X-rays, and microwaves, have demonstrated potential; nevertheless, they are still constrained by issues related to portability, precision, and the expense of the required equipment. Hence, there is a need for a moisture content determination method that is instant, portable, non-destructive, inexpensive, and precise. This study explores the use of deep learning and machine vision to predict moisture content classes from RGB images of wood chips. A large-scale image dataset comprising 1,600 RGB images of wood chips has been collected and annotated with ground truth labels, utilizing the results of the oven-drying technique. Two high-performing neural networks, MoistNetLite and MoistNetMax, have been developed leveraging Neural Architecture Search (NAS) and hyperparameter optimization. The developed models are evaluated and compared with state-of-the-art deep learning models. Results demonstrate that MoistNetLite achieves 87% accuracy with minimal computational overhead, while MoistNetMax exhibits exceptional precision with a 91% accuracy in wood chip moisture content class prediction. With improved accuracy and faster prediction speed, our proposed MoistNet models hold great promise for the wood chip processing industry.