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.

"Robots need to be able to walk fast and efficiently on natural, uneven terrain so they can go everywhere humans can go, but maybe shouldn't," said Emily Lathrop, the paper's first author and a Ph.D. student at the Jacobs School of Engineering at UC San Diego. The researchers will present their findings at the RoboSoft conference which takes place virtually May 15 to July 15, 2020. "Usually, robots are only able to control motion at specific joints," said Michael T. Tolley, a professor in the Department of Mechanical and Aerospace Engineering at UC San Diego and senior author of the paper. "In this work, we showed that a robot that can control the stiffness, and hence the shape, of its feet outperforms traditional designs and is able to adapt to a wide variety of terrains." The feet are flexible spheres made from a latex membrane filled with coffee grounds.

What's the next $300 billion industry to be disrupted by technology? For background, engineered wood is the technical name for any wood product (like particle board) that is created by bonding wood chips into different shapes using an adhesive. It's much cheaper than using a solid piece of wood, and can be used to make anything from an Ikea desk to kitchen countertops. Materialize.X, launching today at TechCrunch Disrupt SF 2017, has two new products that it thinks will revolutionize the $300 billion a year engineered wood market. A lot of engineered wood is created using an adhesive called urea-formaldehyde, which has recently been labeled by the FDA as a toxic carcinogen.