Detecting dried Oudemansiella raphanipies using RGB imaging: A case study

Abstract

Fresh Oudemansiella raphanipies are rich in protein and prone to deterioration during natural storage. Hot air drying is typically used to extend shelf life. However, some O. raphanipies may not dry during the actual drying process because of different placements, which can adversely affect the entire product. To detect dryness, we introduced ORD-Net, a model based on RGB images captured using a smartphone camera. Incorporating residual hybrid dilated convolution and efficient channel attention modules, ORD-Net outperforms mainstream algorithms, achieving high accuracy, precision, recall, F1-score, and a Matthews coefficient of 85.3, 82.2, 91.2, 86.5%, and 0.709, respectively. Additionally, experiments on various CPUs confirm its adaptability for industrial use, with AMD Ryzen CPUs performing optimally with an average processing time of 0.67 s for a single target. This study proposes a convenient and cost-effective identification method that can effectively determine the drying status of O. raphanipies, thus laying the foundation for the high-throughput identification of the drying conditions of postharvest O. raphanipies. The data and methods used in this study are available at https://github.com/Jmin-Zhao/ORDNet.

Keywords:

• Drying
• Oudemansiella raphanipies
• image processing
• deep learning
• machine vision

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Additional information

Funding

This study was funded by the National Natural Science Foundation of China (62362039).