Fish Freshness Classification Using Combined Deep Learning Model

Rayan, Muhammad Abu; Rahim, Abdur; Rahman, Abir; Marjan, Md. Abu; Ali, U. A. Md. Ehsan

doi:10.1109/acmi53878.2021.9528138

Cited by 15 publications

(3 citation statements)

References 5 publications

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“…The process extracts features using VGG-16 neural network architecture, and bi-directional long-short-term memory is used to build a machine learning model. The proposed model has achieved 98% accuracy in testing [8].…”

Section: Imam Bonjol Atas Streetmentioning

confidence: 92%

Classification Freshness of Red Snapper (Lutjanus Campechanus) Based on Eye Image Using Convolutional Neural Network

Subhan,

Nursakinah

2022

EPI-IJE

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Indonesia is a maritime country where fish is the most widely extracted and consumed marine natural resource, one of which is snapper. Snapper contains high protein. Therefore, it is suitable for health. Red snapper or Lutjanus campechanus is one economical fish with a broad market share. Red snapper is a demersal fish group that ranks third with the most exported commodities after tuna and shrimp. In addition, snapper is one of the most common consumption fish in Indonesia. Therefore, the community needs to be able to identify the freshness of the fish. Fish freshness detection is done manually by touching the fish's body, eyes, and gills. However, this can cause accidental damage to the fish parts, which will be very detrimental. Several studies on identifying fish freshness explain that the VGGNet-16 Architecture on the Convolutional Neural Network algorithm is superior in its modeling performance. This research uses a different fish object, a red snapper object, with two different architectures from several previous studies, namely the Le-Net15 and VGGNet-16 architecture. This research focuses on the eye image carried out through the pre-processing data stage by cutting the fish body, followed by augmentation to reproduce the image data without losing its essence before training the dataset. The model will be trained using the Adam optimization method with very fresh and not fresh predictions. The experimental results of the classification of two classes of red snapper freshness using 600 fish images show that VGGNet-16 achieves the best performance compared to the LeNet-5 architecture, where the classification accuracy reaches 98.40%.

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Section: Imam Bonjol Atas Streetmentioning

confidence: 92%

Classification Freshness of Red Snapper (Lutjanus Campechanus) Based on Eye Image Using Convolutional Neural Network

Subhan,

Nursakinah

2022

EPI-IJE

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“…The "Fish Freshness Classification" dataset, available on the public "Kaggle" platform (Rayan et al, 2021), comprises 4476 images capturing fresh and stale fish from various angles. The fish images have a resolution of 224x224 pixels.…”

Section: Methodsmentioning

confidence: 99%

Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study

Kılıçarslan,

Hız Çiçekliyurt,

Kılıçarslan

2024

Turkish JAF Sci.Tech.

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Fish is regarded as an important protein source in human nutrition due to its high concentration of omega-3 fatty acids In traditional global cuisine, fish holds a prominent position, with seafood restaurants, fish markets, and eateries serving as popular venues for fish consumption. However, it is imperative to preserve fish freshness as improper storage can lead to rapid spoilage, posing risks of potential foodborne illnesses. To address this concern, artificial intelligence techniques have been utilized to evaluate fish freshness, introducing a deep learning and machine learning approach. Leveraging a dataset of 4476 fish images, this study conducted feature extraction using three transfer learning models (MobileNetV2, Xception, VGG16) and applied four machine learning algorithms (SVM, LR, ANN, RF) for classification. The synergy of Xception and MobileNetV2 with SVM and LR algorithms achieved a 100% success rate, highlighting the effectiveness of machine learning in preventing foodborne illness and preserving the taste and quality of fish products, especially in mass production facilities.

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“…Gambar 3. Diagram Blok Arsitektur VGG-16 [24] VGG-16 pertama kali dikenalkan pada tahun 2014 oleh Karen Simonyan dan Andrew Zisserman dalam makalah Deep Convolution Network yang berisi pengenalan gambar skala besar. VGG-16 merupakan model pra-pelatihan Convolution Neural Network (CNN) [16].…”

Section: Visual Geometry Group (Vgg)-16unclassified

Perbandingan Kinerja Deep Learning Dalam Pendeteksian Kerusakan Biji Kopi

et al. 2022

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Coffee is one of the most consumed beverages today. The coffee beans are first sorted by the farmers. This is because there are many types of coffee beans that differ in terms of shape and texture. After sorting, farmers must detect whether the coffee beans are damaged or not. The process is still done manually by coffee farmers so it takes a long time and results in errors due to lack of knowledge about coffee. In addition, efforts are also being made to improve the quality of the coffee beans which will affect the selling value of the coffee beans. Based on these problems, this study aims to design a deep learning model to detect coffee bean damage and evaluate the architecture of ResNet-34 and VGG-16. The classification model built using a Convolutional Neural Network (CNN) is expected to be able to know a better architecture and be able to detect damaged or normal coffee beans accurately and precisely

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Fish Freshness Classification Using Combined Deep Learning Model

Cited by 15 publications

References 5 publications

Classification Freshness of Red Snapper (Lutjanus Campechanus) Based on Eye Image Using Convolutional Neural Network

Classification Freshness of Red Snapper (Lutjanus Campechanus) Based on Eye Image Using Convolutional Neural Network

Fish Freshness Detection Through Artificial Intelligence Approaches: A Comprehensive Study

Perbandingan Kinerja Deep Learning Dalam Pendeteksian Kerusakan Biji Kopi

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