大闸蟹养殖大数据分析模型和应用进展

doi:10.19788/j.issn.2096-6369.210106

Abstract

Abstract:

The Chinese mitten crab is a well-known aquaculture product in China. With the development of a big data platform supporting the Chinese mitten crab industry chain, big data technologies can be used to generate insights and value from the data produced during crab culturing, becoming an important way to promote the upgrade of the hairy crab industry chain. Analysis models using big data can clarify the relationships among vast amounts of data and reveal the internal laws of crab farming. A study of these models examines whether big data technologies can be applied to crab culturing to successfully upgrade the crab industry. This paper summarizes the status and performance of several big data models focused on important problems in crab farming. These problems include water quality prediction and early warning, water quality control, feeding strategy, disease detection, behavior recognition, and product quality identification. Several big data platforms currently used in the crab industry are reviewed and the challenges and opportunities of big data analytic models in crab farming are discussed. The review highlights the foundations and advantages in thoroughly applying big data technologies in the crab farming industry, but also highlights challenges in solving practical problems, and the lack of targeted and associated research findings. To reinforce the application of big data technologies, the special characteristics and practical requirements of the industry should be considered when building analysis models, and more intelligent services should be provided on a big data platform for the crab industry. The construction of an effective big data platform on crabs can guide the construction of big data platforms for other agricultural products.

Key words: chinese mitten crab, big data of specific product, data analysis, data model, aquatic big data, smart aquaculture, agricultural big data, smart agriculture

CLC Number:

TP311.1

Qingling Duan, Yiran Liu, Xinhui Zhou, Ni Ren, Daoliang Li. Progress in Analysis Models and the Application of Big Data in Chinese Mitten Crab Culturing[J].Journal of Agricultural Big Data, 2021, 3(1): 56-65.

Figures/Tables 3

References 64

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分类方法	依据	特点	代表参考文献	代表参考文献预测精度
影响因素个数	单一	构建方法简单	[13]	RMSE=0.5697^[13] MAPE=6.35%^[13]
影响因素个数	多个	考虑更多影响因素	[15-17]	RMSE=0.3232^[17]
监测点个数和分布	一维	数据采集容易	[18-20]	RMSE=0.1973^[18] MAPE=9.22%^[18]
监测点个数和分布	三维	能准确全面反映出溶解氧的空间分布规律	[21-23]	RMSE=0.348^[22]
模型构建方法	模型驱动	考虑水质参数间互相作用机理	[14]	AE<1.13mg/L^[14]
模型构建方法	数据驱动	考虑数据本身的特点	[24-25]	RMSE=0.2570^[24] MAPE=2.99%^[24]

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Progress in Analysis Models and the Application of Big Data in Chinese Mitten Crab Culturing

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