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| 基于近红外机器视觉的鱼类摄食强度评估方法研究 | |
| 作者姓名： | 周超 徐大明 吝凯 陈澜 张松 孙传恒 杨信廷 |
| 作者单位： | 北京农业信息技术研究中心,北京 100097 国家农业信息化工程技术研究中心,北京 100097 农产品质量安全追溯技术及应用国家工程实验室,北京 100097 农业部农业信息技术重点实验室,北京 100097 |
| 摘 要： | 在水产养殖中,鱼类的摄食强度可以反映其食欲,准确客观地评估鱼类的摄食强度对指导投喂和生产实践具有重要意义。针对当前鱼类摄食强度评估过程中存在的人工观测效率低、客观性不强的问题,本研究以实现鱼类食欲的自动客观分析为目的,提出了一种基于近红外机器视觉的游泳型鱼类摄食强度的评估方法。首先,利用近红外工业相机搭建了近红外图像采集系统,采集了鱼类摄食过程中的图像。经过一系列图像处理步骤后,利用灰度共生矩阵提取摄食图像的纹理特征变量信息,包括对比度、能量、相关性、逆差距和熵等。之后,将这5个特征变量作为输入向量构建了模型的数据集,并训练了支持向量机分类器。为了提高模型分类的准确率,利用网格搜索法选取支持向量机分类器的最优惩罚系数c和核函数参数g。最后利用训练好的模型将鱼类的摄食强度分为弱、一般、中和强4类,最终实现了鱼类摄食强度的评估。试验结果表明,图像纹理可以较好地描述鱼类摄食过程中的行为变化,正确识别4类摄食强度的准确率达到87.78%,且不需要考虑水花等对成像质量的影响,具有较强的适应性。本方法可用于鱼类食欲的自动客观评估,为后续投喂决策提供理论依据和方法支持。 |
| 关 键 词： | 水产养殖 近红外机器视觉 鱼类摄食强度评估 支持向量机 投喂决策 |
| 收稿时间： | 2018-11-20 |
Evaluation of fish feeding intensity in aquaculture based on near-infrared machine vision |
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| Authors: | Zhou Chao Xu Daming Lin Kai Chen Lan Zhang Song Sun Chuanheng Yang Xinting |
| Institution: | Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China National Engineering Laboratory for Agri-product Quality Traceability, Beijing 100097, China Key Laboratory for Information Technologies in Agriculture, Ministry of Agriculture, Beijing 100097, China |
| Abstract: | In aquaculture, feeding intensity can directly reflect the appetite of fish, which is of great significance for guiding feeding and productive practice. However, most of the existing fish feeding intensity evaluation methods have problems of low observation efficiency and low objectivity. In this study, a fish feeding intensity evaluation method based on near-infrared machine vision was proposed to achieve an automatic objective evaluation of fish appetite. Firstly, a near-infrared image acquisition system was built by using near-infrared industrial camera. After a series of image processing steps, the gray level co-occurrence matrix was used to extract the texture feature variable information of the image, including contrast, energy, correlation, inverse gap and entropy. Then the data set were constructed by using these five feature variables as input vectors, and the support vector machine classifier was trained. Among them, the optimal penalty coefficient c and kernel function parameter g were selected by grid search. Finally, the trained images were used to classify the feeding images of fish. And ultimately, the evaluation of fish feeding intensity was realized. The results show that the accuracy of the evaluation could reach 87.78%. In addition, this method does not need to consider the impact of reflections, sprays and other factors on image processing results, so it has strong adaptability and can be used for automatic and objective evaluation of fish appetite, thus provide theoretical basis and methodological support for subsequent feeding decisions. |
| Keywords: | aquaculture near-infrared machine vision feeding activity evaluation support vector machine feeding decision |
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