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This study proposes a wood board recognition method that integrates laser speckle technology with deep learning. Conventional photography and laser speckle imaging were employed to capture wood board images before and after modification treatments under both normal lighting and adverse conditions（including darkness and defocusing）. A corresponding dataset was then constructed. Classification experiments were conducted using the ResNet34 deep learning model. The results show that the ResNet34 model achieves high recognition accuracy when classifying laser speckle datasets and maintains good performance even under adverse environmental conditions. Furthermore, by introducing a convolutional block attention module（CBAM）to optimize the ResNet34 convolutional neural network, the classification accuracy for laser speckle images reached 93.29%. The combination of laser speckle technology and deep learning provides a low-environmental-requirement, efficient, and promising approach for wood board classification.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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