Institute of Complex Systems, South Bohemian Research Centre of Aquaculture and Biodiversity of Hydrocenoses, Faculty of Fisheries and Protection of Waters, University of South Bohemia in České Budějovice, Zámek 136, Nové Hrady 37 333, Czech Republic.
Institut National de la Recherche Agronomique (INRA), UE 0937 PEIMA (Pisciculture Expérimentale INRA des Monts d'Arrée), 29450 Sizun, France.
Sensors (Basel). 2018 Mar 29;18(4):1027. doi: 10.3390/s18041027.
The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout () were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and -Nearest neighbours (-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The -NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.
本研究的主要目的是开发一种新的客观方法，使用基于图像的特征来评估不同饮食对活体鱼皮的影响。总共将 160 条虹鳟鱼（Oncorhynchus mykiss）分为两组，一组喂食基于鱼粉的饮食（80 条鱼），另一组喂食 100%植物性饮食（80 条鱼），然后使用消费级数码相机对其进行拍照。提取了 23 种颜色特征和 4 种纹理特征。使用四种不同的分类方法评估鱼类饮食，包括随机森林（RF）、支持向量机（SVM）、逻辑回归（LR）和 -最近邻（-NN）。具有径向基核的 SVM 提供了最佳分类器，正确分类率（CCR）为 82%，Kappa 系数为 0.65。尽管 LR 和 RF 方法的准确性不如 SVM，但它们的分类准确率分别为 75%和 70%。-NN 的分类模型准确性最低（40%）。总的来说，可以得出结论，消费级数码相机可以作为快速、准确和非侵入性的传感器，根据虹鳟鱼的饮食对其进行分类。此外，基于养殖过程中获得的图像特征与鱼类饮食之间存在密切关联。这些程序可以作为一种非侵入性、准确和精确的方法，通过评估饮食对鱼皮的影响来监测养殖过程中的鱼类状况。
