Mobile e-detection of Banyuwangi’s citrus fruit maturity using k-nearest neighbor
Keywords:
E-Detection, Citrus Fruit, Maturity, AndroidAbstract
Banyuwangi is the largest oranges-producing city in East Java, and the orange produced is Siamese citrus fruit. Siamese is Banyuwangi local citrus fruit often found at the harvest time and has a sweet taste. To determine the citrus fruit level, people can detect it from the color and texture. In this modern era, people can use an application to determine the citrus fruits' maturity level. From the elements of color and texture, this research will add the citrus fruit's contours, namely the pore size of the citrus fruit and the distance between the curve of the tip of the orange. Taking pictures of citrus fruits will be following the application stages that will be used as the image of inputting the data. The detection is then conducted using the K-NN method based on several criteria based on the input image after the feature extraction process. The feature extraction stages are segmentation, normalization, thresholding, and thinning, which will be produced in several criteria: the maximum RGB value, the minimum RGB value, pore size, and the distance between the tip's curve of the orange. The research results that have been carried out are based on the research stages to get a similarity percentage following the inputted data. The E-Detection application can provide information to citrus farmers, especially beginner citrus farmers, to know the level of fruit maturity oranges to be harvested.References
H. Prabowo, “Deteksi Kondisi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna Pada Ruang Warna Rgb Berbasis Android,†J. Elektron. Sist. Inf. dan Komput., vol. 3, no. 2, pp. 9–19, 2017.
V. Andrearczyk and P. F. Whelan, “Deep learning in texture analysis and its application to tissue image classification,†in Biomedical texture analysis, Elsevier, 2017, pp. 95–129.
K. Warman, L. A. Harahap, and A. P. Munir, “Identifikasi Kematangan Buah Jeruk dengan Teknik Jaringan Syaraf Tiruan,†J. Rekayasa Pangan dan Pertan, vol. 3, no. 2, pp. 248–253, 2015.
L. Armi and S. Fekri-Ershad, “Texture image analysis and texture classification methods-A review,†arXiv Prepr. arXiv1904.06554, 2019.
M. R. Hassan, R. R. Ema, and T. Islam, “Color image segmentation using automated K-means clustering with RGB and HSV color spaces,†Glob. J. Comput. Sci. Technol., 2017.
X. Zhu et al., “Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN,†Biosyst. Eng., vol. 189, pp. 116–132, 2020.
A. Nazir, R. Ashraf, T. Hamdani, and N. Ali, “Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor,†in 2018 international conference on computing, mathematics and engineering technologies (iCoMET), 2018, pp. 1–6.
V. P. Singh and R. Srivastava, “Improved image retrieval using color-invariant moments,†in 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT), 2017, pp. 1–6.
B. Farou, H. Rouabhia, H. Seridi, and H. Akdag, “Novel approach for detection and removal of moving cast shadows based on rgb, hsv and yuv color spaces,†Comput. Informatics, vol. 36, no. 4, pp. 837–856, 2017.
P. Rosyani, M. Taufik, A. A. Waskita, and D. H. Apriyanti, “Comparison of color model for flower recognition,†in 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 2018, pp. 10–14.
L. Wendling, I. Debled-Rennesson, and H. Nasser, “Multilevel polygonal descriptor matching defined by combining discrete lines and force histogram concepts,†Multimed. Tools Appl., pp. 1–15, 2019.
J. M. Wandeto and B. Dresp-Langley, “Color Sensitivity of The Quantization Error in a Self-Organizing Map: Dataset.â€
J. Xu, J. Miao, Z. Gao, K. Nie, and X. Shi, “Analysis and modeling of quantization error in spike-frequency-based image sensor,†Microelectron. Reliab., vol. 111, p. 113705, 2020.
E. Park, D. Kim, S. Yoo, and P. Vajda, “Precision highway for ultra low-precision quantization,†arXiv Prepr. arXiv1812.09818, 2018.
Y. Zhong, E. Dutkiewicz, Y. Yang, X. Zhu, Z. Zhou, and T. Jiang, “Internet of mission-critical things: human and animal classification—a device-free sensing approach,†IEEE Internet Things J., vol. 5, no. 5, pp. 3369–3377, 2017.
A. M. Tarazona, M. C. Ceballos, and D. M. Broom, “Human relationships with domestic and other animals: one health, one welfare, one biology,†Animals, vol. 10, no. 1, p. 43, 2020.
W. E. Sari, Y. E. Kurniawati, and P. I. Santosa, “Papaya Disease Detection Using Fuzzy Naïve Bayes Classifier,†in 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2020, pp. 42–47.
A. Wajid, N. K. Singh, P. Junjun, and M. A. Mughal, “Recognition of ripe, unripe and scaled condition of orange citrus based on decision tree classification,†in 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018, pp. 1–4.
R. K. Haba and K. C. Pelangi, “Pengelompokan Buah Jeruk menggunakan Naïve Bayes dan Gray Level Co-occurrence Matrix,†Ilk. J. Ilm., vol. 12, no. 1, pp. 17–24, 2020.
Y. Dang, N. Jiang, H. Hu, Z. Ji, and W. Zhang, “Image classification based on quantum K-Nearest-Neighbor algorithm,†Quantum Inf. Process., vol. 17, no. 9, pp. 1–18, 2018.
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