Development of smart sorting machine using artificial intelligence for chili fertigation industries

• Autorzy: Abdul Aziz M. F. , Bukhari W. M. , Sukhaimie M. N. , Izzuddin T.A. , Norasikin M.A. , Rasid A. F. A. , Bazilah N. F.

Identyfikatory

DOI

10.14313/JAMRIS/4-2021/26

• Języki publikacji: EN

Abstrakty

EN

This paper presents an automation process is a need in the agricultural industry specifically chili crops, that implemented image processing techniques and classification of chili crops usually based on their color, shape, and texture. The goal of this study was to develop a portable sorting machine that will be able to segregate chili based on their color by using Artificial Neural Network (ANN) and to analyze the performance by using the Plot Confusion method. A sample of ten green chili images and ten red chili images was trained by using Learning Algorithm in MATLAB program that included a feature extraction process and tested by comparing the performance with a larger dataset, which are 40 samples of chili images. The trained network from 20 samples produced an overall accuracy of 80 percent and above, while the trained network from 40 samples produced an overall accuracy of 85 percent. These results indicate the importance of further study as the design of the smart sorting machine was general enough to be used in the agricultural industry that requires a high volume of chili crops and with other differentiating features to be processed at the same time. Improvements can be made to the sorting system but will come at a higher price.

Słowa kluczowe

EN

precision agriculture; artificial neural network; smart fertigation

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2021
• Tom: Vol. 15, No. 4
• Strony: 44--52
• Opis fizyczny: Bibliogr. 41 poz., rys.

Twórcy

autor

Abdul Aziz M. F.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Bukhari W. M.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Sukhaimie M. N.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Izzuddin T.A.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Norasikin M.A.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Rasid A. F. A.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

autor

Bazilah N. F.

• Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

Bibliografia

• [1] M. W. Khaing, A. M. Win and D. T. Aye, “Automatic Sorting Machine”, International Journal of Science and Engineering Applications, vol. 7, no. 8, 2018, 138–142.
• [2] J. M. Low, W. S. Maughan, S. C. Bee and M. J. Honeywood, “5 - Sorting by colour in the food industry”. In: E. Kress-Rogers and C. J. B. Brimelow (eds.), Instrumentation and Sensors for the Food Industry (Second Edition), 2001, 117–136, 10.1533/9781855736481.1.117.
• [3] J. A. Kodagali and S. Balaji, “Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits A Review”, International Journal of Computer Applications, vol. 50, no. 6, 2012, 6–12, 10.5120/7773-0856.
• [4] S. A. Bini, “Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Computing: What Do These Terms Mean and How Will They Impact Health Care?”, The Journal of Arthroplasty, vol. 33, no. 8, 2018, 2358–2361, 10.1016/j.arth.2018.02.067.
• [5] O. Cruz-Domínguez, J. L. Carrera-Escobedo, C. H. Guzmán-Valdivia, A. Ortiz-Rivera, M. García--Ruiz, H. A. Durán-Muñoz, C. A. Vidales-Basurto and V. M. Castaño, “A novel method for dried chili pepper classification using artificial intelligence”, Journal of Agriculture and Food Research, vol. 3, 2021, 10.1016/j.jafr.2021.100099.
• [6] V. Kakani, V. H. Nguyen, B. P. Kumar, H. Kim and V. R. Pasupuleti, “A critical review on computer vision and artificial intelligence in food industry”, Journal of Agriculture and Food Research, vol. 2, 2020, 10.1016/j.jafr.2020.100033.
• [7] M. M. Sofu, O. Er, M. C. Kayacan and B. Cetişli, “Design of an automatic apple sorting system using machine vision”, Computers and Electronics in Agriculture, vol. 127, 2016, 395–405, 10.1016/j.compag.2016.06.030.
• [8] N. Khuriyati, D. A. Nugroho and N. A. Wicaksono, “Quality assessment of chilies (Capsicum annuum L.) by using a smartphone camera”.In: IOP Conference Series: Earth and Environmental Science, vol. 425, 2020, 10.1088/1755-1315/425/1/012040.
• [9] H. J. G. Opeña and J. P. T. Yusiong, “Automated Tomato Maturity Grading Using ABC-Trained Artificial Neural Networks”, Malaysian Journal of Computer Science, vol. 30, no. 1, 2017, 12–26, 10.22452/mjcs.vol30no1.2.
• [10] M. R. Fiona, S. Thomas, I. J. Maria and B. Hannah, “Identification Of Ripe And Unripe Citrus Fruits Using Artificial Neural Network”. In: Journal f Physics: Conference Series, vol. 1362, 2019, 10.1088/1742-6596/1362/1/012033.
• [11] F. M. A. Mazen and A. A. Nashat, “Ripeness Classification of Bananas Using an Artificial Neural Network”, Arabian Journal for Science and Engineering, vol. 44, no. 8, 2019, 6901–6910, 10.1007/s13369-018-03695-5.
• [12] M. Ataş, Y. Yardimci and A. Temizel, “A new approach to aflatoxin detection in chili pepper by machine vision”, Computers and Electronics in Agriculture, vol. 87, 2012, 129–141, 10.1016/j.compag.2012.06.001.
• [13] W. H. M. Saad, S. A. A. Karim, M. S. J. A. Razak, S. A. Radzi and Z. M. Yussof, “Classification and detection of chili and its flower using deep learning approach”. In: Journal of Physics: Conference Series, vol. 1502, 2020, 10.1088/1742-6596/1502/1/012055.
• [14] W. Zhang, J. Mei and Y. Ding, “Design and Development of a High Speed Sorting System Based on Machine Vision Guiding”, Physics Procedia, vol. 25, 2012, 1955–1965, 10.1016/j.phpro.2012.03.335.
• [15] K. Akila, B. Sabitha, K. Balamurugan, K. Balaji and T. Ashwin Gourav, “Mechatronics System Design for Automated Chilli Segregation”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 8S, 2019, 546–550.
• [16] J. Camacho, R. Lewis and R. S. Dwyer-Joyce, “Wear of a chute in a rice sorting machine”, Wear, vol. 263, no. 1, 2007, 65–73, 10.1016/j.wear.2006.11.052.
• [17] B. Jarimopas and N. Jaisin, “An experimental machine vision system for sorting sweet tamarind”, Journal of Food Engineering, vol. 89, no. 3, 2008, 291–297, 10.1016/j.jfoodeng.2008.05.007.
• [18] S. Sheth, R. Kher and P. Dudhat, “Automatic Sorting System Using Machine vision”. In: Multi Disciplinary International Symposium on Control, Automation & Robotics, 2010.
• [19] C. Kunhimohammed, K. Muhammed Saifudeen, S. Sahna, M. Gokul and S. U. Abdulla, “Automated Color Sorting Machine Using TCS230 Colour Sensor and PIC Microcontroller”, International Technology, vol. 2, no. 2, 2015.
• [20] K. Kumar and S. Kayalvizhi, “Real Time Industrial Colour Shape and Size Detection System Using Single Board”, International Journal of Science, Engineering and Technology Research (IJSETR), vol. 4, no. 3, 2015.
• [21] J. Sobota, R. PiŜl, P. Balda and M. Schlegel, “Raspberry Pi and Arduino boards in control education”, IFAC Proceedings Volumes, vol. 46, no. 17, 2013, 7–12, 10.3182/20130828-3-UK2039.00003.
• [22] S. Silva, D. Duarte, R. Barradas, S. Soares, A. Valente and M. J. C. S. Reis, “Arduino recursive backtracking implementation, for a robotic contest”. In: Human-Centric Robotics, 2017, 169–178, 10.1142/9789813231047_0023.
• [23] A. D. Salman and M. A. Abdelaziz, “Mobile Robot Monitoring System based on IoT”, Journal of Xi’An University of Architecture & Technology, vol. 12, no. 3, 2020, 5438–5447, 10.37896/JXAT12.03/501.
• [24] K. Nosirov, S. Begmatov, M. Arabboev, T. Kuchkorov, J. C. Chedjou, K. Kyamakya, P. De Silva and K. Abhiram, “The Greenhouse Control Based-Vision and Sensors”. In: Developments of Artificial Intelligence Technologies in Computation and Robotics, vol. 12, 2020, 1514–1523, 10.1142/9789811223334_0181.
• [25] B. B. Krishnan, P. A. M. Kottalil, A. Anto and B. Alex, “Automatic Sorting Machine”, Journal for Research, vol. 2, no. 4, 2016, 66–70.
• [26] V. Chakole, P. Ilamkar, R. Gajbhiye and S. Nagrale, “Oranges Sorting Using Arduino Microcontroller (A Review)”, International Research Journal of Engineering and Technology (IRJET), vol. 6, no. 2, 2019, 1800–1802.
• [27] X. Meng, “Digital Image Processing Technology Based on MATLAB”. In: Proceedings of the 4th International Conference on Virtual Reality, 2018, 79–82, 10.1145/3198910.3234654.
• [28] B. Siemiątkowska and K. Gromada, “A New Approach to the Histogram-Based Segmentation of Synthetic Aperture Radar Images”, Journal of Automation, Mobile Robotics and Intelligent Systems, 2021, 39–42, 10.14313/JAMRIS/1-2021/5.
• [29] A. D. M. Africa and J. S. Velasco, “Development of a Urine Strip Analyzer Using Artificial Neural Network Using an Android Phone”, ARPN Journal of Engineering and Applied Sciences, vol. 12, no. 6, 2017, 1706–1713.
• [30] J. McCarthy, “What Is Artificial Intelligence?” http://jmc.stanford.edu/articles/whatisai.html,007. Accessed on: 2022-08-30.
• [31] T. Gevorgyan, “Adoption and inclusion of Artificial Intelligence in digitalization strategies of organizations,” Master Thesis, Aalborg University, Copenhagen, 2019.
• [32] M. I. Jordan and T. M. Mitchell, “Machine learning: Trends, perspectives, and prospects”, Science, vol. 349, no. 6245, 2015, 255–260, 10.1126/science.aaa8415.
• [33] A. Moubayed, M. Injadat, A. B. Nassif, H. Lutfiyya and A. Shami, “E-Learning: Challenges and Research Opportunities Using Machine Learning & Data Analytics”, IEEE Access, vol. 6, 2018, 39117–39138, 10.1109/ACCESS.2018.2851790.
• [34] “Use of Decision Trees and Random Forest in achine Learning an Insight into Supervised Learning for Classification Problems,” TechVidvan, https://techvidvan.com/tutorials/supervised--learning/. Accessed on: 2022-08-30.
• [35] S. Luthra, “Machine Learning: An Automated Learning Approach”, International Journal of Computer Engineering and Applications, vol. 12, no. 1, 2018, 156–161.
• [36] A. Mehta, “An Ultimate Guide to Understanding Supervised Learning”, https://www.digitalvidya.com/blog/supervised-learning/. Accessed on: 2022-08-30.
• [37] “12V 28BYJ-48 Stepper Motor + ULN2003 Driver Board,” Cytron, https://my.cytron.io/p-12v28byj-48-stepper-motor-plus-uln2003-driverboard?r=1&gclid=EAIaIQobChMIq5OnjtCd7gIV Cx4rCh3DQAM3EAQYAyABEgKR5PD_BwE. Accessed on: 2022-08-30.
• [38] “In-Depth: Control 28BYJ-48 Stepper Motor with ULN2003 Driver & Arduino,” LastMinuteEngineers.com, https://lastminuteengineers.com/28byj48-stepper-motor-arduino-tutorial/.Accessed on: 2022-08-30.
• [39] “OV7670 VGA Camera Module,” Cytron, https://my.cytron.io/p-ov7670-vga-camera-module?r=1&gclid=EAIaIQobChMIsNmo0MWd7gIVyH4rCh2vewohEAQYAyABEgLijfD_BwE. Accessed on:2022-08-30.
• [40] R. Pelayo, “Arduino Camera (OV7670) Tutorial,”https://www.teachmemicro.com/arduino-camera-ov7670-tutorial/. Accessed on: 2022-08-30.
• [41] C. Sirawattananon, N. Muangnak and W. Pukdee, “Designing of IoT-based Smart Waste Sorting System with Image-based Deep Learning Applications”. In: 2021 18th International Conference on Electrical Engineering/Electronics, Computer,Telecommunications and Information Technology (ECTI-CON), 2021, 383–387, 10.1109/ECTI--CON51831.2021.9454826.