Machine vision based local fish recognition

Sharmin, Israt; Islam, Nuzhat Farzana; Jahan, Israt; Joye, Tasnem Ahmed; Rahman, Md. Riazur; Habib, Md. Tarek

doi:10.1007/s42452-019-1568-z

Cited by 38 publications

(16 citation statements)

References 16 publications

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“…Alongside environmental conditions, some other reasons pose a threat to the Bangladesh water resources. Sharmin et al [1] aimed to identify and classify the freshwater fishes in order to introduce them to the next generations. However, There is not a convincing comparison in their study.…”

Section: Related Workmentioning

confidence: 99%

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An open-set framework for underwater image classification using autoencoders

Akhtarshenas

Toosi

2022

SN Appl. Sci.

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In this paper, we mainly intend to address the underwater image classification problem in an open-set scenario. Image classification algorithms have been mostly provided with a small set of species, while there exist lots of species not available to the algorithms or even unknown to ourselves. Thus, we deal with an open-set problem and extremely high false alarm rate in real scenarios, especially in the case of unseen species. Motivated by these challenges, our proposed scheme aims to prevent the unseen species from going to the classifier section. To this end, we introduce a new framework based on convolutional neural networks (CNNs) that automatically identifies various species of fishes and then classifies them into certain classes using a novel technique. In the proposed method, an autoencoder is employed to distinguish between seen and unseen species. To clarify, the autoencoder is trained to reconstruct the available species with high accuracy and filter out species that are not in our training set. In the following, a classifier based on EfficientNet is trained to classify the samples that are accepted by the autoencoder (AE), i.e. the samples that have small reconstruction error. Our proposed method is evaluated in terms of precision, recall, and accuracy and compared to the state-of-the-art methods utilizing WildFish dataset. Simulation results reveal the supremacy of the proposed method.

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Section: Related Workmentioning

confidence: 99%

“…This in turn degraded the performance of algorithms by lowering accuracy. Due to its tremendous applications, fish species recognition and classification is of great importance in the domains of pattern recognition and image segmentation in oceans and ecosystem monitoring [1].…”

Section: Introductionmentioning

confidence: 99%

An open-set framework for underwater image classification using autoencoders

Akhtarshenas

Toosi

2022

SN Appl. Sci.

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“…Alsmadi et al [13] introduced a fish classification model that utilized the combination between extracted features and statistical measurements. Some works were carried out on fish classification by utilizing the backpropagation algorithm, support vector machines [18], [19]. [8] introduced a deep learning model to classify aquarium fish species and attained an accuracy of 85.59%.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural network-based system for fish classification

Mehmood

Abugabah

Almekhlafi³

et al. 2022

IJECE

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<p>In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely adaptive delta (AdaDelta), stochastic gradient descent (SGD), adaptive momentum (Adam), adaptive max pooling (Adamax), Root mean square propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classification, among others.</p>

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“…They have focused predominantly on the ResNet50 network, a well-known CNN model as a preprepared model in Transfer Learning. Sharmin et al [8] proposed fish determination in freshwater.…”

Section: Introductionmentioning

confidence: 99%

Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

Rajbongshi

Khan

Rahman³

et al. 2021

IJEECS

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<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

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Machine vision based local fish recognition

Cited by 38 publications

References 16 publications

An open-set framework for underwater image classification using autoencoders

An open-set framework for underwater image classification using autoencoders

Deep convolutional neural network-based system for fish classification

Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

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