Color Based Feature Extraction and Backpropagation Neural Network in Tamarind Turmeric Herb Recognition

Mila  Fauziyah; Supriatna Adhisuwignjo; Bagus Fajar Afandi Afandi; Lathifatun Nazhiroh

doi:10.22219/kinetik.v7i2.1432

Issue

Vol. 7, No. 2, May 2022

Issue Published : May 31, 2022

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Color Based Feature Extraction and Backpropagation Neural Network in Tamarind Turmeric Herb Recognition

https://doi.org/10.22219/kinetik.v7i2.1432

Mila Fauziyah

Politeknik Negeri Malang

Supriatna Adhisuwignjo

Politeknik Negeri Malang

Bagus Fajar Afandi Afandi

Department of Electrical Engineering, State Polytechnic of Malang

Lathifatun Nazhiroh

Politeknik Negeri Malang

Corresponding Author(s) : Bagus Fajar Afandi Afandi

bagusfajar02@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 7, No. 2, May 2022
Article Published : May 31, 2022

Abstract

The aim of this paper is finding the optimum image pattern of the tamarind turmeric herb. So far, in the process of producing tamarind turmeric herb, it is not constant in terms of taste and color, which is influenced by maturity and the amount of turmeric. Image pattern recognition will use Backpropagation algorithm applied to typical Content-based image retrieval systems. The main purpose is to apprehend various parts of tamarind turmeric herb in the retrieving processing. The camera is applied to classify the tamarind turmeric herb product, process into 5x5 pixels, and take an average of the RGB value so the stable RGB values will be obtained in each category and used as input for Backpropagation algorithm. The most suitable and the fastest process from the Backpropagation algorithm will be searched and applied in a real-time machine. In this paper will be using two methods, first, train the algorithm using ten data by change neuron, layer, momentum, and learning rate, and the last is testing with ten data. The results obtained from the training and testing algorithm that the two hidden layers can recognize 100% inputs, with three input layers used for R, G, and B value, ten neurons in the first hidden layers and the second hidden layers, one output layer with a parameter used is Learning rate 0.5 and Momentum 0.6. The best image pattern standard for tamarind turmeric herb is dark yellow with RGB values of 255, 102, 32 up to 255, 128, 48.

Keywords

Tamarind Turmeric Herb Feature Extraction Backpropagation RGB Image

Fauziyah, M. ., Adhisuwignjo, S., Afandi, B. F. A., & Nazhiroh, L. (2022). Color Based Feature Extraction and Backpropagation Neural Network in Tamarind Turmeric Herb Recognition. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 7(2). https://doi.org/10.22219/kinetik.v7i2.1432

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References

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F. Kruggel, “A Simple Measure for Acuity in Medical Images,” IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5225–5233, Nov. 2018, doi: 10.1109/TIP.2018.2851673.
M. Yauri-Machaca, B. Meneses-Claudio, N. Vargas-Cuentas, and A. Roman-Gonzalez, Design of a Vehicle Driver Drowsiness Detection System Through Image Processing using Matlab. 2018 IEEE 38th Central America and Panama Convention (CONCAPAN XXXVIII), 2018. doi: 10.1109/CONCAPAN.2018.8596513.
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F. Taqi, F. Al-Langawi, H. Abdulraheem, and M. El-Abd, A cherry-tomato harvesting robot. 2017 18th International Conference on Advanced Robotics (ICAR), 2017. doi: 10.1109/ICAR.2017.8023650.
Y. W. Chen, K. Chen, S. Y. Yuan, and S. Y. Kuo, “Moving Object Counting Using a Tripwire in H.265/HEVC Bitstreams for Video Surveillance,” IEEE Access, vol. 4, pp. 2529–2541, 2016, doi: 10.1109/ACCESS.2016.2572121.
M. F. Ahmad, H. J. Rong, S. S. N. Alhady, W. Rahiman, and W. A. F. W. Othman, Colour tracking technique by using pixy CMUcam5 for wheelchair luggage follower. 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2017. doi: 10.1109/ICCSCE.2017.8284402.
R. ALASCO et al, SoilMATTic: Arduino-Based Automated Soil Nutrient and pH Level Analyzer using Digital Image Processing and Artificial Neural Network. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018. doi: 10.1109/HNICEM.2018.8666264.
M. Rasamuel, L. Khacef, L. Rodriguez, and B. Miramond, Specialized visual sensor coupled to a dynamic neural field for embedded attentional process. ," 2019 IEEE Sensors Applications Symposium (SAS), 2019. doi: 10.1109/SAS.2019.8705979.
P. Bours and K. Helkala, “Face recognition using separate layers of the RGB image,” in Proceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008, 2008, pp. 1035–1042. doi: 10.1109/IIH-MSP.2008.162.
S. Phetnuam and T. Yingthawornsuk, “Classfication of Categorized KMUTT-BKT’s Landscape Images Using RGB Color Feature,” in Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018, Jul. 2018, pp. 327–331. doi: 10.1109/SITIS.2018.00057.
S. Bettahar, A. B. Stambouli, P. Lambert, and A. Benoit, “PDE-based enhancement of color images in RGB space,” IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2500–2512, May 2012, doi: 10.1109/TIP.2011.2177844.
P. K. Mishra, S. Pandey, and S. K. Biswash, “Efficient Resource Management by Exploiting D2D Communication for 5G Networks,” IEEE Access, vol. 4, pp. 9910–9922, 2016, doi: 10.1109/ACCESS.2016.2602843.
P. Pattanasethanon, “Thai botanical herbs and its characteristics: Using artificial neural network,” AFRICAN JOURNAL OF AGRICULTURAL RESEEARCH, vol. 7, no. 2, Jan. 2012, doi: 10.5897/ajarx11.062.
L. Munkhdalai, T. Munkhdalai, K. H. Park, H. G. Lee, M. Li, and K. H. Ryu, “Mixture of Activation Functions with Extended Min-Max Normalization for Forex Market Prediction,” IEEE Access, vol. 7, pp. 183680–183691, 2019, doi: 10.1109/ACCESS.2019.2959789.

References

I. A. A. Widari, S. Mulyani, and B. Admadi. H, “Kunyit Asam And Sinom Beverages Inhibition with a-Glucosidase Enzyme Activity,” Jurnal Rekayasa Dan Manajemen Agroindustri, vol. 2, no. 2, September 2014, pp. 26-35, ISSN: 2503-488X, 2014.

K. I. Dewi and R.B. Wirjatmadi, “Relationship between Vitamin C and Iron Adequacy with Physical Fitness of Pencak Silat Athletes IPSI Lamongan,” Media Gizi Indonesia, vol. 12, no. 2, pp. 134–140, 2017.

H. Setyomomgsoh, Y. Pratiwi, A. Rahmawati, H. M. Wijaya, and R. N. Lina, “Penggunaan Vitamin untuk Meningkatkan Imunitas Tubuh di Masa Pandemi,” Jurnal Pengabdian Kesehatan STIKES Cendekia Utama Kudus, vol. 4, no. 2, pp. 136–150, 2021, [Online]. Available: http://jpk.jurnal.stikescendekiautamakudus.ac.id

S. N. Hidayah, N. Izah, and I. D. Andari, “Peningkatan Imunitas dengan Konsumsi Vitamin C dan Gizi Seimbang Bagi Ibu Hamil Untuk Cegah Corona Di Kota Tegal,” Jurnal ABDINUS : Jurnal Pengabdian Nusantara, vol. 4, no. 1, pp. 170–174, 2020, doi: 10.29407/ja.v4i1.14641.

R. K. Wijayanti, W. D. R. Putri, and N. I.P. Nugrahini, “Effect Proportion of Turmeric (Curcuma longa L.) and Tamarind (Tamarindus indica) on Leather Tamarind-Turmeric Characteristic,” Jurnal Pangan dan Agroindustri, vol. 4, no. 1, 2016.

J. Ridwan, Emanauli, and Sahrial, “Pengaruh Penambahan Ekstrak Kunyit Terhadap Sifat Fisik Kimia Dan Organoleptik Minuman Fungsional Saribuah Perepat (Sonneratia Alba).”

M. Kam and A. Guez, “On the Probabilistic Interpretation of Neural Network Behavior,” in 1987 American Control Conferenc, May 1987, pp. 1968–1972. doi: 10.23919/ACC.1987.4789633.

R. Lippmann, “An Introduction’ to Computing with Neural Nets,” in IEEE ASSP Magazine, 1987. doi: 10.1109/MASSP.1987.1165576.

N. A. Al-Sammarraie, Y. M. H. Al-Mayali, and Y. A. Baker El-Ebiary, “Classification and diagnosis using back propagation Artificial Neural Networks (ANN) algorithm,” 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE), 2018. doi: 10.1109/ICSCEE.2018.8538383.

L. R. Reddy, P. Patel, and S. K. Rajendra, “Utilization of resilient back propagation algorithm and discrete wavelet transform for the differential protection of three phase power transformer,” in 2020 21st National Power Systems Conference, NPSC 2020, Dec. 2020, pp. 1–6. doi: 10.1109/NPSC49263.2020.9331861.

R. Zhu et al., “Back-Propagation Neural Network based on Analog Memristive Synapse,” 2018 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC), 2018. doi: 10.1109/EDSSC.2018.8487059.

Y. Ayyappa, A. Bekkanti, A. Krishna, P. Neelakanteswara, and C. Z. Basha, Enhanced and Effective Computerized Multi Layered Perceptron based Back Propagation Brain Tumor Detection with Gaussian Filtering. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA, 2020. doi: 10.1109/ICIRCA48905.2020.9182921.

H. Mhatre and V. Bhosale, “Super resolution of Astronomical Objects using Back Propagation Algorithm,” 2016 International Conference on Inventive Computation Technologies (ICICT), 2016. doi: 10.1109/INVENTIVE.2016.7824824.

S. Das, A. Wahi, S. Sundaramurthy, N. Thulasiram, and S. Keerthika, “Classification of knitted fabric defect detection using Artificial Neural Networks,” 2019 International Conference on Advances in Computing and Communication Engineering (ICACCE), 2019. doi: 10.1109/ICACCE46606.2019.9079951.

F. N. Fajri, N. Hamid, and R. A. Pramunendar, The recognition of mango varieties based on the leaves shape and texture using back propagation neural network method. 2017 International Conference on Sustainable Information Engineering and Technology (SIET), 2017. doi: 10.1109/SIET.2017.8304101.

S. Ghadge, S. Patankar, and J. Kulkarni, Text Identification in Noncursive English Handwritten Script. 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT, 2018. doi: 10.1109/RTEICT42901.2018.9012291.

J. Chen et al., “Fatigue detection based on facial images processed by difference algorithm,” 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), 2017. doi: 10.2316/P.2017.852-017.

S. Pang et al., “SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation,” IEEE Transactions on Medical Imaging, vol. 40, no. 1, pp. 262–273, Jan. 2021, doi: 10.1109/TMI.2020.3025087.

X. Chen, G. Zhai, J. Wang, C. Hu, and Y. Chen, Color guided thermal image super resolution. 2016 Visual Communications and Image Processing (VCIP), 2016. doi: 10.1109/VCIP.2016.7805509.

F. Kruggel, “A Simple Measure for Acuity in Medical Images,” IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5225–5233, Nov. 2018, doi: 10.1109/TIP.2018.2851673.

M. Yauri-Machaca, B. Meneses-Claudio, N. Vargas-Cuentas, and A. Roman-Gonzalez, Design of a Vehicle Driver Drowsiness Detection System Through Image Processing using Matlab. 2018 IEEE 38th Central America and Panama Convention (CONCAPAN XXXVIII), 2018. doi: 10.1109/CONCAPAN.2018.8596513.

M. A. A. Mosleh, A. A. AL-Yamni, and A. Gumaei, An Automatic Nuclei Cells Counting Approach Using Effective Image Processing Methods. 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), 2019. doi: 10.1109/SIPROCESS.2019.8868753.

F. Taqi, F. Al-Langawi, H. Abdulraheem, and M. El-Abd, A cherry-tomato harvesting robot. 2017 18th International Conference on Advanced Robotics (ICAR), 2017. doi: 10.1109/ICAR.2017.8023650.

Y. W. Chen, K. Chen, S. Y. Yuan, and S. Y. Kuo, “Moving Object Counting Using a Tripwire in H.265/HEVC Bitstreams for Video Surveillance,” IEEE Access, vol. 4, pp. 2529–2541, 2016, doi: 10.1109/ACCESS.2016.2572121.

M. F. Ahmad, H. J. Rong, S. S. N. Alhady, W. Rahiman, and W. A. F. W. Othman, Colour tracking technique by using pixy CMUcam5 for wheelchair luggage follower. 2017 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), 2017. doi: 10.1109/ICCSCE.2017.8284402.

R. ALASCO et al, SoilMATTic: Arduino-Based Automated Soil Nutrient and pH Level Analyzer using Digital Image Processing and Artificial Neural Network. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2018. doi: 10.1109/HNICEM.2018.8666264.

M. Rasamuel, L. Khacef, L. Rodriguez, and B. Miramond, Specialized visual sensor coupled to a dynamic neural field for embedded attentional process. ," 2019 IEEE Sensors Applications Symposium (SAS), 2019. doi: 10.1109/SAS.2019.8705979.

P. Bours and K. Helkala, “Face recognition using separate layers of the RGB image,” in Proceedings - 2008 4th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2008, 2008, pp. 1035–1042. doi: 10.1109/IIH-MSP.2008.162.

S. Phetnuam and T. Yingthawornsuk, “Classfication of Categorized KMUTT-BKT’s Landscape Images Using RGB Color Feature,” in Proceedings - 14th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2018, Jul. 2018, pp. 327–331. doi: 10.1109/SITIS.2018.00057.

S. Bettahar, A. B. Stambouli, P. Lambert, and A. Benoit, “PDE-based enhancement of color images in RGB space,” IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2500–2512, May 2012, doi: 10.1109/TIP.2011.2177844.

P. K. Mishra, S. Pandey, and S. K. Biswash, “Efficient Resource Management by Exploiting D2D Communication for 5G Networks,” IEEE Access, vol. 4, pp. 9910–9922, 2016, doi: 10.1109/ACCESS.2016.2602843.

P. Pattanasethanon, “Thai botanical herbs and its characteristics: Using artificial neural network,” AFRICAN JOURNAL OF AGRICULTURAL RESEEARCH, vol. 7, no. 2, Jan. 2012, doi: 10.5897/ajarx11.062.

L. Munkhdalai, T. Munkhdalai, K. H. Park, H. G. Lee, M. Li, and K. H. Ryu, “Mixture of Activation Functions with Extended Min-Max Normalization for Forex Market Prediction,” IEEE Access, vol. 7, pp. 183680–183691, 2019, doi: 10.1109/ACCESS.2019.2959789.

Issue

Vol. 7, No. 2, May 2022

Color Based Feature Extraction and Backpropagation Neural Network in Tamarind Turmeric Herb Recognition

Corresponding Author(s) : Bagus Fajar Afandi Afandi

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