Identification of Rotten Carrots Using Image Processing with Edge Detection and Convolution Techniques

Muhammad Syofian

doi:10.37034/medinftech.v3i4.53

Authors

Muhammad Syofian PT. Indonesia Comnet Plus

DOI:

https://doi.org/10.37034/medinftech.v3i4.53

Keywords:

Convolution Techniques, Edge Detection, Enhancement, Image Processing, Rotten Carrots

Abstract

Carrot is one of the agricultural commodities with high nutritional value and a significant market demand. However, its quality can deteriorate due to various factors, one of which is rotting. Early detection of rotting carrots is crucial to prevent economic losses and maintain product quality. The main problem in identifying rotten carrots lies in the need for high precision and the time-consuming nature of manual methods. To address this issue, this research develops an automated method for detecting rotten carrots using image processing techniques. In this study, edge detection and convolution techniques are employed as the primary approaches in image analysis. Edge detection is used to recognize contours and boundaries in carrot images, while convolution techniques are applied to identify patterns of damage and texture differences between rotten and healthy carrots. The research findings indicate that this method is capable of detecting rotten carrots with high accuracy, making it reliable as a tool for sorting and quality assurance in carrot processing.

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References

E. Sobari and F. Fathurohman, “Efektifitas Penyiangan Terhadap Hasil Tanaman Wortel (Daucus carota L.) Lokal Cipanas Bogor,” Jurnal Biodjati, vol. 2, no. 1, pp. 1-8, May 2017.

F. Al Azami, A. A. Riadi, and E. Evanita, “Klasifikasi kualitas wortel menggunakan metode k-nearest neighbor berbasis Android,” Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika), vol. 7, no. 1, pp. 36–39, 2022.

P. H. Marpaung, F. Siburian, and L. P. Nainggolan, “Analisis Yang Mempengaruhi Rotasi Tanaman Ercis (Pisum Sativum L) Ke Tanaman Wortel (Daucus Carota L) Kecamatan Dolat Raya, Kabupaten Karo,” Jurnal Agroteknosains, vol. 6, no. 1, p. 81, Apr. 2022, doi: 10.36764/ja.v6i1.757.

K. Anwariyah, “Deteksi Objek Nomor Kendaraan Pada Citra Kendaraan Bermotor,” JTIM : Jurnal Teknologi Informasi dan Multimedia, vol. 1, no. 4, pp. 311–317, Feb. 2020, doi: 10.35746/jtim.v1i4.65.

J. Jumadi, Y. Yupianti, and D. Sartika, “Pengolahan Citra Digital Untuk Identifikasi Objek Menggunakan Metode Hierarchical Agglomerative Clustering,” JST (Jurnal Sains dan Teknologi), vol. 10, no. 2, pp. 148–156, Nov. 2021, doi: 10.23887/jst-undiksha.v10i2.33636.

J. Zhang et al., “A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches,” Artificial Intelligence Review, vol. 55, no. 4, pp. 2875–2944, Sep. 2021, doi: 10.1007/s10462-021-10082-4.

I. Arief Wisky and Sumijan, “Deteksi Tepi untuk Mendeteksi Kondisi Otak Menggunakan Metode Prewitt,” Jurnal Teknologi, vol. 12, no. 2, pp. 34–39, Dec. 2022, doi: 10.35134/jitekin.v12i2.68.

D. Prasetya, Y. D. Lestari, dan A. Budiman, “Perbaikan kualitas citra dengan kombinasi metode contrast stretching dan metode konvolusi,” Prosiding Seminar Nasional Teknologi Informasi & Komunikasi, vol. 1, no. 1, pp. 437–442, 2020.

M. Mirbabaie, S. Stieglitz, and N. R. J. Frick, “Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction,” Health and Technology, vol. 11, no. 4, pp. 693–731, May 2021, doi: 10.1007/s12553-021-00555-5.

J. Saputra, Y. Sa’adati, V. Y. P. Ardhana, and M. Afriansyah, “Klasifikasi kematangan buah alpukat mentega menggunakan metode k-nearest neighbor berdasarkan warna kulit buah,” Resolusi: Rekayasa Teknik Informatika dan Informasi, vol. 3, no. 5, pp. 347–354, 2023.

X. Liang, R. Zhang, M. L. Gleason, and G. Sun, “Sustainable Apple Disease Management in China: Challenges and Future Directions for a Transforming Industry,” Plant Disease, vol. 106, no. 3, pp. 786–799, Mar. 2022, doi: 10.1094/pdis-06-21-1190-fe.

S. Avidan and A. Shamir, “Seam carving for content-aware image resizing,” ACM Transactions on Graphics, vol. 26, no. 3, p. 10, Jul. 2007, doi: 10.1145/1276377.1276390.

D. Chandra and S. Sembiring, “Meningkatkan Efisiensi Pemrosesan Citra Untuk Klasifikasi Kualitas Biji Jagung Berbasis Tekstur,” Jurnal Ilmiah Multidisiplin Ilmu Komputer, Vol. 1, no. 2, pp. 60-73, Feb. 2024.

A. Nurmasani and Y. Pristyanto, “Algoritme Stacking Untuk Klasifikasi Penyakit Jantung Pada Dataset Imbalanced Class,” Pseudocode, vol. 8, no. 1, pp. 21–26, Mar. 2021, doi: 10.33369/pseudocode.8.1.21-26.

Z. Wang, Z. Liang, X. Li, and H. Li, “Indoor Visible Light Positioning Based on Improved Particle Swarm Optimization Method With Min-Max Algorithm,” IEEE Access, vol. 10, pp. 130068–130077, 2022, doi: 10.1109/access.2022.3228543.

K. Yang, Z. Liang, R. Liu, and W. Li, “RSS-Based Indoor Localization Using Min-Max Algorithm With Area Partition Strategy,” IEEE Access, vol. 9, pp. 125561–125568, 2021, doi: 10.1109/access.2021.3111650.

W. A. Saputra, M. Z. Naf’an, and A. Nurrochman, “Implementasi Keras library dan convolutional neural network pada konversi formulir pendaftaran siswa,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 3, no. 3, pp. 524–531, Dec. 2019, doi: 10.29207/resti.v3i3.1338.

M. A. Masril and R. Noviardi, “Analisa morfologi dilasi untuk perbaikan kualitas citra deteksi tepi pada pola batik menggunakan operator Prewitt dan Laplacian of Gaussian,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 6, pp. 1052–1057, 2020.

S. Saifullah, “Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur,” Systemic: Information System and Informatics Journal, vol. 5, no. 2, pp. 53–60, Mar. 2020, doi: 10.29080/systemic.v5i2.798.

T. M. Hameedi and G. A. Kaya, “Enhanced Data Hiding Using Some Attribute of Color Image,” 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), pp. 1–5, Jun. 2023, doi: 10.1109/hora58378.2023.10156670.

N. Wulan Dari, “Identifikasi Deteksi Tepi Pada Pola Wajah Menerapkan Metode Sobel, Roberts dan Prewitt,” Bulletin of Information Technology (BIT), vol. 3, no. 2, pp. 85–91, Jun. 2022, doi: 10.47065/bit.v3i2.271.

R. J. Pally and S. Samadi, “Application of image processing and convolutional neural networks for flood image classification and semantic segmentation,” Environmental Modelling & Software, vol. 148, p. 105285, Feb. 2022, doi: 10.1016/j.envsoft.2021.105285.

A. Pranata and E. Z. Astuti, “Pengolahan citra berbasis deteksi tepi Prewitt pada gambar gigi manusia,” Eksplora Informatika, vol. 6, no. 2, pp. 98–105, 2017.

Y. Wu, X. Feng, and G. Chen, “Plant Leaf Diseases Fine-Grained Categorization Using Convolutional Neural Networks,” IEEE Access, vol. 10, pp. 41087–41096, 2022, doi: 10.1109/access.2022.3167513.

M. Ikhsan, S. Supiyandi, and A. W. Hakiki, “Analisis perbandingan metode histogram equalization dan Gaussian filter untuk perbaikan kualitas citra,” Journal of Science and Social Research, vol. 7, no. 2, pp. 487–492, 2024, doi:10.54314/jssr.v7i2.1865.