Eggs Classification based on Egg Shell Image using K-Nearest Neighbors Classifier

Rachmawanto, Eko Hari; Sari, Christy Atika; Villadelfiya, Rivalda; Setiadi, De Rosal Ignatius Moses; Rijati, Nova; Kartikadarma, Etika; Doheir, Mohamed; Astuti, Setia

doi:10.1109/isemantic50169.2020.9234305

Cited by 10 publications

(11 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The gray-scale density values of SO eggshell in the median plane direction are significantly higher than GO and CO eggs. The trend is similar to the results observed in Rachmawanto et al [35]. They found that the eggshell tendency to break increased as the egg quality decreased (from good quality to rotten and defective eggs).…”

Section: Discussionsupporting

confidence: 90%

Application of Visual Radiographic Analysis of Quality Grade of Table Eggs

2021

View full text Add to dashboard Cite

Digital radiography is currently the main method of medical imaging diagnosis. It also has a wide range of applications across different fields. This study used radiation to conduct non-destructive visual imaging, and further established a quantitative analysis for visual gray-scale images to determine changes in the quality of eggs. Eggs of the same weight with three quality classes were chosen according to the egg labels available on the market. Furthermore, a general medical X-ray digital imaging system was used to apply two-dimensional digital radiography. A photometric interpretation of monochrome gray-scale imaging established by the Digital Imaging and Communications in Medicine (DICOM) standard was used to conduct a quantitative stratification analysis of the matrix data visualization, along with one-way analysis of variance (ANOVA) for quantitative statistics of the gray-scale values for the three structures, i.e., shell, air cell, and yolk. The statistical results showed that X-ray digital gray-scale images and a quantitative stratification analysis of the matrix data visualization results are less easily identified based on visual differences. In the quantitative statistical results of the one-way ANOVA gray-scale values, the whole-egg and in-egg quantitative matrix analysis both show p < 0.05. In the analysis of egg freshness, the quantitative statistics of the percentage of space occupied by the air cell in the eggs also showed p < 0.05. In addition, the results of the freshness of each egg were graded. The quality and freshness of the eggs can be quantitatively analyzed through radiographic imaging. The results of this study will provide a more scientific and quantitative reference for the quality and freshness of agricultural products in the future.

show abstract

Section: Discussionsupporting

confidence: 90%

Application of Visual Radiographic Analysis of Quality Grade of Table Eggs

2021

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Textural features may include smooth, rough texture, spots, or other characteristics of the skin surface [21]. One of the texture features that is popular and has high performance is the Gray Level Co-Occurrence Matrix (GLCM), this feature has been widely used in various classification studies such as skin diseases [22], [23], breast cancer [24], plant root [25], fruit quality [26], and fruit quality [27]. This proves that GLCM's performance is proven to be reliable for classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm

Araaf,

Nugroho,

Setiadi

2023

J. Comput. Theor. Appl.

View full text Add to dashboard Cite

Skin is the largest organ in humans, it functions as the outermost protector of the organs inside. Therefore, the skin is often attacked by various diseases, especially cancer. Skin cancer is divided into two, namely benign and malignant. Malignant has the potential to spread and increase the risk of death. Skin cancer detection traditionally involves time-consuming laboratory tests to determine malignancy or benignity. Therefore, there is a demand for computer-assisted diagnosis through image analysis to expedite disease identification and classification. This study proposes to use the K-nearest neighbor (KNN) classifier and Gray Level Co-occurrence Matrix (GLCM) to classify these two types of skin cancer. Apart from that, the average filter is also used for preprocessing. The analysis was carried out comprehensively by carrying out 480 experiments on the ISIC dataset. Dataset variations were also carried out using random sampling techniques to test on smaller datasets, where experiments were carried out on 3297, 1649, 825, and 210 images. Several KNN parameters, namely the number of neighbors (k)=1 and distance (d)=1 to 3 were tested at angles 0, 45, 90, and 135. Maximum accuracy results were 79.24%, 79.39%, 83.63%, and 100% for respectively 3297, 1649, 825, and 210. These findings show that the KNN method is more effective in working on smaller datasets, besides that the use of the average filter also has a significant contribution in increasing the accuracy.

show abstract

Eggs Classification based on Egg Shell Image using K-Nearest Neighbors Classifier

Cited by 10 publications

References 14 publications

Application of Visual Radiographic Analysis of Quality Grade of Table Eggs

Application of Visual Radiographic Analysis of Quality Grade of Table Eggs

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

Comprehensive Analysis and Classification of Skin Diseases based on Image Texture Features using K-Nearest Neighbors Algorithm

Contact Info

Product

Resources

About