Research of Water Body Turbidity Classification Model for Aquiculture Based on Transfer Learning
| Autor: | Yanxuan Huang, Jianhua Zheng, Zitao Qiu, Gaolin Yang, Leian Liu, Shuangyin Liu, Guihuang Hong |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Physics: Conference Series. 1757:012004 |
| ISSN: | 1742-6596 1742-6588 |
| DOI: | 10.1088/1742-6596/1757/1/012004 |
| Popis: | Fodder, fish manure, and pond sludge will seriously affect the turbidity of the water body in aquaculture. How to quickly and online judge the turbidity of the water body is very important for realizing efficient, low-cost, and accurate control of aquaculture. In view of the shortcomings of traditional detection methods, a transfer learning method based on ResNet deep learning network model is proposed to realize water body turbidity classification, and two transfer learning methods of parameter partially frozen and completely unfrozen are designed based on ResNet18.Subsequently, the turbidity data set of aquaculture water quality was constructed, and the data set was enhanced by image cropping, image flipping, random scaling, and other methods. The experimental results show that when all parameters are not frozen, the transfer learning method can achieve the best class effect, and the accuracy rate is 0.9686, which can provide an effective method for online detection of aquaculture water body turbidity. |
| Databáze: | OpenAIRE |
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