Betawi Traditional Food Image Detection using ResNet and DenseNet

Setyono, Noer Fitria Putra; Chahyati, Dina; Fanany, Mohamad Ivan

doi:10.1109/icacsis.2018.8618175

Cited by 13 publications

(5 citation statements)

References 3 publications

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“…Hasil ujicoba menggunakan optimasi RmsProp menghasilkan nilai akurasi terbaik dengan arsitektur ResNet50 yaitu 88,5%, sensitivitas 85,1% dan spesifisitas 92,6%. Penelitian selanjutnya mengenai ResNet-50 oleh Setyono [13] yang mendapatkan hasil bahwa ResNet-50 mengungguli ResNet101 dan ResNet152 dalam hal running time.…”

Section: Pendahuluanunclassified

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Lasniari

Jasril

Sanjaya

et al. 2022

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Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.

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Section: Pendahuluanunclassified

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Lasniari

Jasril

Sanjaya

et al. 2022

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“…ResNet peringkat pertama dalam deteksi ImageNet, lokalisasi ImageNet, deteksi COCO, dan segmentasi COCO. Arsitektur ResNet dibagi menjadi beberapa arsitektur : ResNet 18, 34, 50, 101 dan 152 [16].…”

Section: Pendahuluanunclassified

Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi

Lasniari

Jasril

Sanjaya

et al. 2022

JNKTI

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Abstrak - Kasus kecurangan pedagang mencampur daging sapi dengan daging babi masih terjadi hingga saat ini. Membedakan daging sapi dan babi dapat dilakukan dengan mengamati secara langsung satu persatu, tetapi hal ini dapat dilakukan oleh para ahli, Tetapi secara kasat mata masih sulit membedakannya. Perilaku pedagang seperti ini sangat merugikan konsumen khususnya pemeluk agama Islam karena berkaitan dengan makanan yang halal atau haram. Pada penelitain ini menggunakan metode Deep Learning untuk klasifikasi citra dengan Convolutional Neural Network (CNN) arsitektur ResNet-50. Jumlah data sebanyak 457 citra yang terbagi menjadi 3 kelas, yaitu daging babi, daging oplosan dan daging sapi. Setiap kelas memiliki ukuran gambar yang sama yaitu 300 x 300 pixel. Pembagian data menggunakan split data dengan perbandingan 70% data uji : 30% data uji, 80% data latih : 20% data uji, dan 90% data latih : 10% data uji. Hasil dari pengujian model dengan Confusion Matrix menunjukkan performa klasifikasi tertinggi dengan 100% accuracy, 100% precision, dan 100% recall, pada data citra asli dengan penggunaan batch size 32, 0.001 learning rate, epoch 75 dan split data 90% : 10%.Kata kunci: Convolutional Neural Network, Daging Babi dan Sapi, Deep Learning, Klasifikasi Citra, ResNet Abstract - Traders mixing beef and pork are still committing fraud today. Although professionals can discern between beef and pork by watching them one by one, it is still impossible to do so with the naked eye. This kind of behavior is very detrimental to consumers, especially Muslims because it is related to halal or haram food. This research uses Deep Learning method to classify images with Convolutional Neural Network (CNN) ResNet-50 architecture. The number of data is 457 images which are divided into 3 classes, namely pork, mixed meat and beef. Each class has the same image size, which is 300 x 300 pixels. data distribution using split data with a comparison of 70% training data: 30% test data, 80% training data: 20% test data, and 90% training data: 10% test data. The results of model testing using the Confusion Matrix show the highest classification performance with 100% accuracy, 100% precision, and 100% recall, on the original image data using batch size 32, 0.001 learning rate, epoch 75 and split data 90%: 10%..Kata kunci: Convolutional Neural Networ, Deep Learning, Image Classification, Pork and Beef, ResNet

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“…Transfer Learning approaches such as a ResNET have been found to be successful in classifying images (He et al, 2016;Marsden et al, 2017;Setyono et al, 2018). A ResNET is a CNN with a skip connection, which is also known as an identity shortcut connection.…”

Section: Transfer Learningmentioning

confidence: 99%

“…Evaluation metrics used in image identification are typically accuracy, precision, recall, F1-score and convergence time (Al-Sarayreh et al, 2018;Larsen et al, 2014;Ropodi et al, 2015;Setyono et al, 2018;Wang et al, 2019). Accuracy and F1 scores are described in Eq.…”

Section: Experimental Setup and Evaluationmentioning

confidence: 99%

Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines

Prakash¹,

Berry²,

Roantree³

et al. 2021

Journal of Animal Science

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The identification of different meat cuts for labelling and quality control on production lines is still largely a manual process. As a result, it is a labor-intensive exercise with the potential for error but also bacterial cross-contamination. Artificial intelligence is used in many disciplines to identify objects within images but these approaches usually require a considerable volume of images for training and validation. The objective of this study was to identify five different meat cuts from images and weights collected by a trained operator within the working environment of a commercial Irish beef plant. Individual cut images and weights from 7987 meats cuts extracted from Semimembranosus muscles (i.e., Topside muscle), post-editing, were available. A variety of classical neural networks and a novel Ensemble machine learning approaches were then tasked with identifying each individual meat cut; performance of the approaches was dictated by accuracy (the percentage of correct predictions); precision (the ratio of correctly predicted objects relative to the number of objects identified as positive), and recall (also known as true positive rate or sensitivity). A novel Ensemble approach outperformed a selection of the classical neural networks including convolutional neural network (CNN) and residual network (ResNET). The accuracy, precision, and recall for the novel Ensemble method were 99.13%, 99.00%, and 98.00%, respectively, while that of the next best method were 98.00%, 98.00%, and 95.00%, respectively. The Ensemble approach, which requires relatively few gold-standard measures, can readily be deployed under normal abattoir conditions; the strategy could also be evaluated in the cuts from other primals or indeed other species.

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Betawi Traditional Food Image Detection using ResNet and DenseNet

Cited by 13 publications

References 3 publications

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra

Pengaruh Hyperparameter Convolutional Neural Network Arsitektur ResNet-50 Pada Klasifikasi Citra Daging Sapi dan Daging Babi

Using artificial intelligence to automate meat cut identification from the semimembranosus muscle on beef boning lines

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