The classification of fish species has become an essential task for marine ecologists and biologists for the estimation of large quantities of fish variants in their own environment and also to supervise their population changes. Different conventional classification is expensive, time-consuming, and laborious. Scattering and absorption of light in deep sea atmosphere achieves a very low-resolution image and becomes highly challenging for the recognition and classification of fish variants. Then, the performance rate of existing computer vision methods starts to reduce underwater because of highly indistinct features and background clutter of marine species. The attained classification issues can be resolved using deep structured models, which are highly recommended to enhance the performance rate in fish species classification. But, only a limited amount of fish datasets is available, which makes the system more complex, and also, they need enormous amounts of datasets to perform training. So, it is essential to develop an automated and optimized system to detect, categorize, track, and minimize manual interference in fish species classification. Thus, this paper aims to suggest a new fish species classification model by the optimized recurrent neural network (RNN) and feature fusion. Initially, standard underwater images are acquired from a standard database. Then, the gathered images are pre-processed for cleaning and enhancing the quality of images using “contrast limited adaptive histogram equalization (CLAHE) and histogram equalization”. Then, the deep feature extractions are obtained using DenseNet, MobileNet, ResNet, and VGG16, where the gathered features are given to the new phase optimal feature selection. They are performed with a new heuristic algorithm called “modified mating probability-based water strider algorithm (MMP-WSA)” that attains the optimal features. Further, the optimally selected features are further fed to the feature fusion process, where the feature fusion is carried out using the adaptive fusion concept. Here, the weights are tuned using the designed MMP-WSA. In addition, the fused features are sent to the classification phase, where the classification is performed using developed FishRNFuseNET, in which the parameters of the RNN are tuned by developed MMP-WSA for getting accurate classified outcomes. The proposed method is an effective substitute for time-consuming and strenuous approaches in human identification by professionals, and it turned as a benefit to monitor the biodiversity of fish in their place.

鱼类物种的分类已成为海洋生态学家和生物学家的一项重要任务，以估计其自身环境中的大量鱼类变种并监督其种群变化。不同的传统分类方法成本高、费时、费力。深海大气中光的散射和吸收获得了非常低分辨率的图像，这对于鱼类变种的识别和分类变得极具挑战性。然后，由于海洋物种的高度模糊特征和背景杂乱，现有计算机视觉方法的性能在水下开始降低。所获得的分类问题可以使用深度结构化模型来解决，强烈建议使用深度结构化模型来提高鱼类分类的性能。但是，只有有限数量的鱼类数据集可用，这使得系统更加复杂，而且它们需要大量的数据集来进行训练。因此，有必要开发一种自动化和优化的系统来检测、分类、跟踪并最大限度地减少鱼类分类中的人工干扰。因此，本文旨在通过优化的循环神经网络（RNN）和特征融合提出一种新的鱼类分类模型。最初，从标准数据库获取标准水下图像。然后，使用“对比度有限自适应直方图均衡（CLAHE）和直方图均衡”对收集的图像进行预处理，以进行清洁和增强图像质量。然后，使用DenseNet、MobileNet、ResNet和VGG16获得深层特征提取，其中收集的特征被赋予新阶段最优特征选择。它们使用一种名为“改进的基于交配概率的水黾算法（MMP-WSA）”的启发式算法来执行，该算法获得了最佳特征。此外，最佳选择的特征被进一步馈送到特征融合过程，其中使用自适应融合概念来执行特征融合。这里，使用设计的 MMP-WSA 调整权重。此外，融合的特征被发送到分类阶段，使用开发的 FishRNFuseNET 进行分类，其中 RNN 的参数由开发的 MMP-WSA 进行调整，以获得准确的分类结果。所提出的方法可以有效替代专业人员进行人类身份识别中耗时且费力的方法，并且有利于监测鱼类的生物多样性。