The dataset WoodChip-MC consists of 1,600 RGB images of wood chips collected from two different sources. The dataset has images from 2 sources: each source has 10 moisture levels. One of the sources is subdivided into two batches. These images were acquired by an industrial camera (Hotpet 8MP USB Industrial Camera with Sony IMX179 Sensor) while keeping the wood chips in an aluminum container. The images were manually labeled through the oven-drying process. The wet wood chips were kept in an oven dryer (set at 105 ◦C) for 24 hours, and then moisture content was calculated from the weight difference. The dataset, at the time of publication, is the largest publicly available data for wood chip moisture content determination through computer vision. With the WoodChip-MC dataset, a performance benchmark of deep learning models has been developed for moisture content class prediction. Detailed model benchmarking and performance results are given in an accompanying journal paper: Rahman, A., Street, J., Wooten, J., Marufuzzaman, M., Gude, V. G., Buchanan, R., & Wang, H. (2025). MoistNet: Machine vision-based deep learning models for wood chip moisture content measurement. Expert Systems with Applications, 259, 125363. https://doi.org/10.1016/j.eswa.2024.125363 Other studies related to the dataset are as follows: Rahman, A., Marufuzzaman, M., Street, J., Wooten, J., Gude, V. G., Buchanan, R., & Wang, H. (2024). A comprehensive review on wood chip moisture content assessment and prediction. Renewable and Sustainable Energy Reviews, 189, 113843. https://doi.org/10.1016/j.rser.2023.113843 Rahman, A., Marufuzzaman, M., Street, J. T., Wooten, J., Gude, V. G., & Wang, H. (2023). An Interpretable Deep Learning Model forWood Chip Moisture Content Prediction. In IISE Annual Conference. Proceedings (pp. 1-6). Institute of Industrial and Systems Engineers (IISE). Rahman, A., Street, J., Wooten, J., Marufuzzaman, M., Wang, H., Gude, V.G., Buchanan, R., 2025. Interpretable wood chip moisture content prediction through texture analysis. Expert Systems with Applications , 126989. doi:https://doi.org/10.1016/j.eswa.2025.126989. If you use the dataset, please cite the dataset or the journal article above. Thank you. Funding Support: This work is supported by the Sustainable Bioeconomy through Biobased Products and Engineering for Agricultural Production and Processing programs, project award no. 2020-67019-30772 and 2022-67022-37861, from the U.S. Department of Agriculture’s National Institute of Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.