Design and Performance Evaluation of Semi-Automatic Fish Cutting Machine for Industry

Kamaruzzaman, Kafiza Ahmad; Mahfurdz, Azrul; Hashim, Mokhtar; Bidin, Mohamed Noorazman

doi:10.1088/1757-899x/864/1/012112

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…Motor speed 755 adalah mesin listrik yang mengubah energi listrik menjadi energi mekanik. Sebagian besar motor listrik beroperasi melalui interaksi antara medan magnet motor dan arus listrik dalam lilitan kawat untuk menghasilkan gaya dalam bentuk torsi yang diterapkan pada poros motor [11]. Pada penelitian ini akan digunakan motor speed 755 untuk menjalankan alat tersebut.…”

Section: Motor Speed 755unclassified

Desain dan Implementasi Automatic Fish Machine Sebagai Solusi Pembersih dan Pemotong Ikan Otomatis Berbasis Arduino

et al. 2021

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Indonesia memiliki luas wilayah perairan sebesar 74,2% dari total luas keseluruhan membuat Indonesia mempunyai potensi sumber daya yang melimpah dari sektor laut, terutama di bidang perikanan. Mata pencarian yang sangat berhubungan dengan dengan ikan adalah nelayan dan pedagang ikan. Sebelum diolah, sisik dan isi perut ikan harus dibersihkanterlebih dahulu. Namun, pedagang ikan membutuhkan waktu yang cukup lama untuk membersihkan seekor ikan menggunakan pisau sehingga dibutuhkan sebuah alat yang dapat membersihkan dan memotong secara otomatis. Penelitian ini merancang dan mengimplementasikan suatu automatic fish machine. Mikrokontroler yang digunakan pada alat ini adalah Arduino AtMega32, sensor Infrared (IR) untuk mendeteksi ikan yang masuk ke alat, dan LCD menampilkan nilai sensor dan kecepatan motor. Kemudian terdapat driver motor yang digunakan untuk menggerakkan motor yang terhubung ke gigi pembersih. Untuk membersihkan sisik dan memotong ikan digunakan bilah pisau pembersih. Alat ini telah diujikan dengan memperhatikan karakteristik dari ikan seperti ukuran ikan dan jenis sisik. Hasil dari penelitian ini menunjukkan bahwa automatic fish machine mampu mempermudah dan mempercepat produksi pembersihan dan pemotongan ikan khususnya ikan Nila. Tebal ikan yang dapat dibersihkan oleh automatic fish machine dengan ukuran minimal 20 mm sampai 60 mm. Hasil pembersih dan pemotong ikan yang dihasilkan dari automatic fish machine adalah 10 ikan/menit.

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Section: Motor Speed 755unclassified

Desain dan Implementasi Automatic Fish Machine Sebagai Solusi Pembersih dan Pemotong Ikan Otomatis Berbasis Arduino

et al. 2021

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“…These processes also require a large number of trained workers. As the amount of catch has rapidly increased through the rapid growth of the fishing industry, manual fish trimming performed by responsible workers will incur an increased cost [29].…”

Section: Introductionmentioning

confidence: 99%

Machine Vision Based Fish Cutting Point Prediction for Target Weight

Jang¹,

Seo²

2023

Computers, Materials &Amp; Continua

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Food processing companies pursue the distribution of ingredients that were packaged according to a certain weight. Particularly, foods like fish are highly demanded and supplied. However, despite the high quantity of fish to be supplied, most seafood processing companies have yet to install automation equipment. Such absence of automation equipment for seafood processing incurs a considerable cost regarding labor force, economy, and time. Moreover, workers responsible for fish processing are exposed to risks because fish processing tasks require the use of dangerous tools, such as power saws or knives. To solve these problems observed in the fish processing field, this study proposed a fish cutting point prediction method based on AI machine vision and target weight. The proposed method performs threedimensional (3D) modeling of a fish's form based on image processing techniques and partitioned random sample consensus (RANSAC) and extracts 3D feature information. Then, it generates a neural network model for predicting fish cutting points according to the target weight by performing machine learning of the extracted 3D feature information and measured weight information. This study allows for the direct cutting of fish based on cutting points predicted by the proposed method. Subsequently, we compared the measured weight of the cut pieces with the target weight. The comparison result verified that the proposed method showed a mean error rate of approximately 3%.

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“…Moreover, with the growth of the fishing industry, the amount of catches is also increasing rapidly. Consequently, manual trimming will gradually require more costs [5]. Catering companies that handle large quantities of food also handle numerous fish, which have a high supply and demand.…”

Section: Introductionmentioning

confidence: 99%

A method for predicting the cutting points using random sample consensus partitioning technique and AI machine vision.

Lee

2023

ijmst

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Food processing and manufacturing companies that handle large amounts of fish want their fish to be packaged and delivered in uniform quantities. In particular, manual labor is performed by skilled workers for fish that are difficult to trim. Moreover, some fish processing sites employ partially automated processing machines. However, the structure of these machines is too simple to process fish with diverse patterns. Furthermore, these machines exhibit a large amount of error for the target weight. To improve the working environment of food processing and manufacturing companies by solving the aforementioned problems, this study proposes a technique for predicting the cutting points of a fish for each weight by adopting image processing and machine learning. The proposed technique employs a variety of image processing techniques and random sample consensus partitioning to extract the 3D model of the fish and its length, maximum major and minor axes, and volume information from the image of the fish. The model trained with the extracted 3D features and the measured weight information can predict the cutting points for the desired weight from the input fish image. The performance evaluation results of the proposed method indicated that there is an average error of less than 3% between the target and predicted weights. This error level is considered significantly better than 8%, which is the permissible error level in fish processing sites. It is expected that the proposed technique will significantly contribute to the development of an automated cutting system that considers the weight by integrating the technique with the cutting machine and conveyor belt.

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Design and Performance Evaluation of Semi-Automatic Fish Cutting Machine for Industry

Cited by 7 publications

References 11 publications

Desain dan Implementasi Automatic Fish Machine Sebagai Solusi Pembersih dan Pemotong Ikan Otomatis Berbasis Arduino

Desain dan Implementasi Automatic Fish Machine Sebagai Solusi Pembersih dan Pemotong Ikan Otomatis Berbasis Arduino

Machine Vision Based Fish Cutting Point Prediction for Target Weight

A method for predicting the cutting points using random sample consensus partitioning technique and AI machine vision.

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