[1]李济泽,位威,张凯凯.基于机器视觉的养殖鱼摄食行为识别方法[J].福建工程学院学报,2022,20(04):378-382.[doi:10.3969/j.issn.1672-4348.2022.04.012]
　LI Jize,WEI Wei,ZHANG Kaikai.Recognition method of fish feeding behavior based on machine vision[J].Journal of FuJian University of Technology,2022,20(04):378-382.[doi:10.3969/j.issn.1672-4348.2022.04.012]
点击复制

基于机器视觉的养殖鱼摄食行为识别方法()
分享到：

《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第20卷
期数:
2022年04期
页码:
378-382
栏目:
出版日期:
2022-08-25

文章信息/Info

Title:
Recognition method of fish feeding behavior based on machine vision
作者:
李济泽; 位威; 张凯凯
福建工程学院机械与汽车工程学院
Author(s):
LI Jize;  WEI Wei;  ZHANG Kaikai
School of Mechanical and Automotive Engineering, Fujian University of Technology
关键词:
养殖鱼; 摄食行为; 机器视觉; 图像纹理; 支持向量机
Keywords:
farmed fish;  feeding behavior;  machine vision;  images texture;  support vector machine
分类号:
S951.2；TP391.4
DOI:
10.3969/j.issn.1672-4348.2022.04.012
文献标志码:
A
摘要:
利用机器视觉系统获取养殖鱼摄食图像纹理特征来识别养殖鱼群的摄食行为。 从养殖鱼摄食图像上直接提取能表征鱼群摄食行为的20 维特征，通过归一化、PCA 降维和支持向量机训练获得养殖鱼摄食行为识别模型，以实现对养殖鱼摄食行为的识别。 结果表明，提出方法的平均精确度为92.3%、假负率7.34%、假正率4.15%，为指导养殖鱼智能投饵提供了参考。
Abstract:
The texture features of the feeding images of farmed fish acquired by the machine vision system were used to identify the feeding behavior of farmed fish. Twenty-dimensional features that can characterize the fish feeding behavior were extracted from the fish feeding images. The fish feeding behavior recognition model was obtained from normalization, PCA dimensionality reduction and support vector machine training, so as to realize the identification of the fish feeding behavior. Experimental results show that the average precision of the proposed method is 92.3%, the false negative rate is 7.34%, and the false positive rate is 4.15%, which provides reference for instructing fish swarm intelligent feeding.

参考文献/References:

[1] 张丰登. 基于循环水养殖大口黑鲈摄食行为反馈的自适应投饲系统研究[D]. 杭州: 浙江大学, 2019.[2] 钱阳. 基于图像动态获取的水产养殖智能投饵机控制系统研究[D]. 镇江: 江苏大学, 2017.[3] PARRA L, SENDRA S, GARCA L, et al. Design and deployment of low-cost sensors for monitoring the water quality and fish behavior in aquaculture tanks during the feeding process[J]. Sensors, 2018, 18（3）: 750. [4] BALLESTER-MOLT M, SANCHEZ-JEREZ P, CEREZO-VALVERDE J, et al. Particulate waste outflow from fish-farming cages. How much is uneaten feed?[J]. Marine Pollution Bulletin, 2017, 119（1）: 23-30.[5] SADOUL B, MENGUES P E, FRIGGENS N C, et al. A new method for measuring group behaviours of fish shoals from recorded videos taken in near aquaculture conditions[J]. Aquaculture, 2014, 430: 179-187.[6] 陈彩文. 基于计算机视觉的鱼群摄食行为分析研究[D]. 太原: 太原理工大学, 2017.[7] 陈志鹏, 陈明. 基于光流法与图像纹理特征的鱼群摄食行为检测[J]. 南方农业学报, 2019, 50（5）: 1141-1148.[8] 周超, 徐大明, 吝凯, 等. 基于近红外机器视觉的鱼类摄食强度评估方法研究[J]. 智慧农业, 2019, 1（1）: 76-84.[9] 郭强. 基于计算机视觉的循环水养殖镜鲤的摄食状态检测方法研究[D]. 上海: 上海海洋大学, 2018.[10] 洪继光. 灰度-梯度共生矩阵纹理分析方法[J]. 自动化学报, 1984, 10（1）: 22-25.[11] ZHOU C, XU D M, CHEN L, et al. Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision[J]. Aquaculture, 2019, 507: 457-465.[12] 周超. 基于近红外机器视觉的鱼类智能投喂决策方法研究[D]. 北京: 北京理工大学, 2018.

更新日期/Last Update: 2022-08-25