Crack on Eggshell Detection System Based on Image Processing Technique

Narin, Burin; Buntan, Sunanta; Chumuang, Narumol; Ketcham, Mahasak

doi:10.1109/iscit.2018.8587980

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…Retakan atau ketidaknormalan pada cangkang dapat berdampak pada lapisanlapisan telur lainnya. Cangkang yang retak dapat memungkinkan masuknya mikroorganisme berbahaya yang berpotensi merusak kandungan telur [9], [10], [11].…”

Section: Tinjauan Pustaka 21 Kualitas Telur Ayamunclassified

Penerapan Convolutional Neural Network Untuk Deteksi Kualitas Telur Ayam Ras Berdasarkan Warna Cangkang

Nurdiyansyah,

Fatriana Kadir,

Akbar

et al. 2024

Mnemonic

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Sebelum dijual atau dikonsumsi, telur ayam harus dievaluasi untuk kualitasnya, terutama dari segi warna dan kondisi cangkangnya. Dalam penelitian ini, digunakan metode Convolutional Neural Network (CNN) untuk mengklasifikasikan warna cangkang telur ayam. Dengan penggunaan CNN diharapkan dapat mengembangkan model Convolutional Neural Network (CNN) yang dapat mengklasifikasikan kualitas telur ayam secara akurat dengan memberikan rekomendasi parameter yang optimal untuk mencapai performa terbaik. Dataset terdiri dari 120 butir telur yang dibagi menjadi tiga kategori: coklat, krem, dan putih. Pengolahan data melibatkan preprocessing image dengan teknik enhancement data untuk memperluas variasi data tanpa kehilangan informasi penting. Selama pengujian, tiga parameter utama dievaluasi: optimizer (Adam, SGD, RMSprop), learning rate (0.01, 0.001, 0.0001), dan epoch (20, 50, 100). Hasil penelitian menunjukkan bahwa kombinasi terbaik untuk performa optimal adalah dengan menggunakan Adam optimizer, learning rate 0,01, dan 100 epoch. Rasio data 80:20 memberikan hasil terbaik, menekankan pentingnya proporsi data yang baik dalam pembentukan model yang akurat. Analisis parameter learning rate menunjukkan bahwa nilai 0.01 memberikan performa terbaik untuk semua proporsi data, sementara hasil terbaik pada nilai epoch diperoleh dengan 100 epoch untuk semua proporsi data. Dengan demikian, penelitian ini menegaskan bahwa CNN efektif dalam mengklasifikasikan kualitas telur berdasarkan warna cangkang, yang dapat memberikan kemudahan bagi pedagang dan peternak telur dalam memilih telur berkualitas.

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Section: Tinjauan Pustaka 21 Kualitas Telur Ayamunclassified

Penerapan Convolutional Neural Network Untuk Deteksi Kualitas Telur Ayam Ras Berdasarkan Warna Cangkang

Nurdiyansyah,

Fatriana Kadir,

Akbar

et al. 2024

Mnemonic

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“…Videos or images have been analyzed and used for studies on broilers including bodyweight [ 20 , 21 ], health status [ 22 , 23 ], behavior [ 24 ], flock movement [ 25 ], and locomotion/activity [ 3 , 26 ]. The technology has also been used in the poultry sector for research into butchering [ 27 ], carcass and meat monitoring [ 28 , 29 ], and egg quality analysis [ 30 , 31 ].…”

Section: Related Workmentioning

confidence: 99%

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

Volkmann

Zelenka

Devaraju

et al. 2022

Sensors

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Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks. In a preliminary study, images of turkeys were annotated by labelling potential injuries. These were used to train a network for injury detection. Here, we applied a keypoint detection model to provide more information on animal position and indicate injury location. Therefore, seven turkey keypoints were defined, and 244 images (showing 7660 birds) were manually annotated. Two state-of-the-art approaches for pose estimation were adjusted, and their results were compared. Subsequently, a better keypoint detection model (HRNet-W48) was combined with the segmentation model for injury detection. For example, individual injuries were classified using “near tail” or “near head” labels. Summarizing, the keypoint detection showed good results and could clearly differentiate between individual animals even in crowded situations.

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“…Computer vision has been widely used in various processes of different poultry production systems. It includes automation of the house management, behavior, and welfare [11,[29][30][31][32][33][34][35][36][37][38][39][40], disease detection [28,[41][42][43][44][45][46][47], weight measurement [27,[48][49], slaughtering process [50][51], carcass quality [52][53][54][55], and egg examination [56][57][58][59][60][61][62][63][64][65]. On the other hand, computer vision also popular on other livestock monitoring, such as pig [73][74][75][76][77][78][79][80], sheep or cattle …”

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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Crack on Eggshell Detection System Based on Image Processing Technique

Cited by 9 publications

References 8 publications

Penerapan Convolutional Neural Network Untuk Deteksi Kualitas Telur Ayam Ras Berdasarkan Warna Cangkang

Penerapan Convolutional Neural Network Untuk Deteksi Kualitas Telur Ayam Ras Berdasarkan Warna Cangkang

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

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