MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement
<|reference_start|>MoistNet: Machine Vision-based Deep Learning Models for Wood Chip Moisture Content Measurement: 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.<|reference_end|>
@article{rahman2024moistnet:,
    title={MoistNet: Machine Vision-based Deep Learning Models for Wood Chip
  Moisture Content Measurement},
    author={Abdur Rahman, Jason Street, James Wooten, Mohammad Marufuzzaman, Veera
  G. Gude, Randy Buchanan, Haifeng Wang},
    journal={arXiv preprint arXiv:2409.04920},
    year={2024},
    doi={10.1016/j.eswa.2024.125363},
    archivePrefix={arXiv},
    eprint={2409.04920},
    primaryClass={cs.CV cs.AI}
}