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Identification of Rice Variety Using Geometric Features and Neural Network
https://doi.org/10.22146/ijccs.48203 Wahyu Srimulyani(1*), Aina Musdholifah(2)
(1) Master’s Program of Computer Science, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, Universitas Gadjah Mada
(*) Corresponding Author
Abstract
Indonesia has many food varieties, one of which is rice varieties. Each rice variety has physical characteristics that can be recognized through color, texture, and shape. Based on these physical characteristics, rice can be identified using the Neural Network. Research using 12 features has not optimal results. This study proposes the addition of geometry features with Learning Vector Quantization and Backpropagation algorithms that are used separately.
The trial uses data from 9 rice varieties taken from several regions in Yogyakarta. The acquisition of rice was carried out using a camera Canon D700 with a kit lens and maximum magnification, 55 mm. Data sharing is carried out for training and testing, and the training data was sharing with the quality of the rice. Preprocessing of data was carried out before feature extraction with the trial and error thresholding process of segmentation. Evaluation is done by comparing the results of the addition of 6 geometry features and before adding geometry features.
The test results show that the addition of 6 geometry features gives an increase in the value of accuracy. This is evidenced by the Backpropagation algorithm resulting in increased accuracy of 100% and 5.2% the result of the LVQ algorithm.
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[1] Parveen Z, Alam M.A., and Shakir H, “Assesssment of Quality of Rice Grain Using Optical and Imgae Processing Technique”, International Conference on Communication Computing and Digital Systems, 2017 [Online]. Available : https://ieeexplore.ieee.org /document/7918940. [Accesed: 25-July-2019]
[2] Sumaryanti, L., Mushdolifah, A., and Hartati, S., “Digital Image Based Identification of Rice Variety Using Image Processing And Neural Network”, Indonesian Journal of Electrical Engineering, Vol.16, No.1, P.182-190, 2015 [Online]. Available : https://www.semanticscholar.org/paper/Digital-Image-Based-Identification-of-Rice-Variety-Sumaryanti-Musdholifah/82fe813a73069e4433ccdabc692b4fef77b8376f. [Accesed: 20-July-2019]
[3] Pazoki A.R., Farokhi F., and Pazoki Z., “Classification of Rice Grain Varieties Using Two Artificial Neural Networks (MLP and Neuro-Fuzzy)”, The Journal of Animal & Plant Sciences, Vol 24, No 1, p. 336-343, 2014 [Online]. Available : https://www.semanticscholar.org/paper/Classification-of-rice-grain-varieties-using-two-Pazoki-Farokhi/9c7908f327b76b1818d4f9123d4d27d98fe2424b. [Accessed: 20-July-2019]
[4] Golpur I, Parian J.A., and Chayjan R.A., “Identification and Clasification of Bulk Paddy, Brown, and White Rice Cultivars with Colour Features Extraction using Image Analysis and Neural Network”, Czech J.Food Sci, Vol. 32, No.3, P.280-287, 2014 [Online]. Available : https://www.agriculturejournals.cz/publicFiles/238_2013. [Accessed: 20-July-2019]
[5] Nagoda N and Ranathunga L, “Rice Sample Segmentation and Classification Using Image Processing and Support Vector Machine”, International Conference on Industrial and Information System (ICIIS), 13th, 2018 [Online]. Available : https://ieeexplore.ieee.org /document/8721312. [Accessed: 25-July-2019]
[6] Siddique M.A.B., Arif.R.B., and Khan.M.M.R., “Digital Image Segmentation in Matlab: A Brief Study on OTSU’s Image Thresholding”, International Conference on Innovation in Engineering and Technology (ICIET), 2018 [ Online]. Available : https://ieeexplore.ieee.org/document/8660942. [Accessed: 25-July-2019]
[7] Youlian Z., Cheng H., Kun Z., and Lingjiao P., “Face Detection method using template feature and skin color feature in RGB color space”, The 27th Chinese Control and Decision Conference, 2015 [Online]. Available : https://ieeexplore.ieee.org /document/7161913. [Accessed: 25-July-2019]
[8] Nandi C.S., Tudu B, and Koley C,“A Machine Vision-Based Maturity Prediction System for Sorting of Harvested Mangoes”, IEEE Transactions on Instrumentation and Measurement., Vol.63, No.7, 2014 [Online]. Available:https://ieeexplore.ieee.org/document/6730653. [Accessed: 25-July-2019]
[9] Syaban K and Harjoko A, “Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network”, Indonesian Journal of Computing and Cybernetics Systems (IJCCS), Vol.10, No.2, 2016 [Online]. Avaliable : https://jurnal.ugm.ac.id/ijccs/article /16628/11686. [Accessed: 27-July-2019]
[10] Ardiansyah and Rainarli E, “Implementasi Q-Learning dan Backpropagation pada Agen yang Memainkan Permainan Flappy Bird”, Jurnal Nasional Teknik Elekto dan Teknologi Informasi (JNTETI), Vol.6, No.1, 2017 [Online]. Available: http://ejnteti.jteti.ugm.ac.id/index.php/JNTETI/article/287/216. [Accessed: 27-July-2019]
[11] Khatri S.K., Dutta S, Johri P, “Recognizing images of handwritten digits using learning vector quantization artificial neural network”, International Conference on Reliability Infocom Technologies and Optimization (ICRITO), Vol.10, 2015 [Online]. Available: https://ieeexplore.ieee.org/document/7359307. [Accessed: 27-July-2019]
DOI: https://doi.org/10.22146/ijccs.48203
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