Near infrared computer vision and neuro-fuzzy model-based feeding decision system for fish in aquaculture

文献类型： 外文期刊

第一作者： Zhou, Chao

作者： Zhou, Chao;Lin, Kai;Xu, Daming;Chen, Lan;Guo, Qiang;Sun, Chuanheng;Yang, Xinting;Zhou, Chao;Lin, Kai;Xu, Daming;Chen, Lan;Guo, Qiang;Sun, Chuanheng;Yang, Xinting;Zhou, Chao;Lin, Kai;Xu, Daming;Chen, Lan;Guo, Qiang;Sun, Chuanheng;Yang, Xinting;Zhou, Chao

作者机构：

关键词： Aquaculture; Feeding behavior; Feeding optimization; Image processing; Adaptive network-based fuzzy inference system

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：5.565； 五年影响因子：5.494 ）

ISSN： 0168-1699

年卷期： 2018 年 146 卷

页码：

收录情况： SCI

摘要： In aquaculture, the feeding efficiency of fish is of great significance for improving production and reducing costs. In recent years, automatic adjustments of the feeding amount based on the needs of the fish have become a developing trend. The purpose of this study was to achieve automatic feeding decision making based on the appetite of fish. In this study, a feeding control method based on near infrared computer vision and neuro-fuzzy model was proposed. The specific objectives of this study were as follows: (1) to develop an algorithm to extract an index that can describe and quantify the feeding behavior of fish in near infrared images, (2) to design an algorithm to realize feeding decision (continue or stop) during the feeding process, and (3) to evaluate the performance of the method. The specific implementation process of this study was as follows: (1) the quantitative index of feeding behavior (flocking level and snatching strength) was extracted by Delaunay Triangulation and image texture; (2) the adaptive network-based fuzzy inference system (ANFIS) was established based on fuzzy control rules and used to achieve automatically on-demand feeding; and (3) the performance of the method was evaluated by the specific growth rate, weight gain rate, feed conversion rate and water quality parameters. The results indicated that the feeding decision accuracy of the ANFIS model was 98%. In addition, compared with the feeding table, although this method did not present significant differences in promoting fish growth, the feed conversion rate (FCR) can be reduced by 10.77% and water pollution can also be reduced. This system provides an important contribution to realizing the real-time control of fish feeding processes and feeding decision on demand, and it lays a theoretical foundation for developing fine feeding equipment and guiding practice.

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