Automated Conveyor System of Sorting and Grading for Red Chili Pepper (Capsicum annum L.) using Image Processing and Artificial Neural Network
DOI:
https://doi.org/10.23960/jtep-l.v13i4.1320-1333Abstract
This research aims to design an automatic sorting and grading tool driven by color sensor processed through image processing and artificial neural networks (ANN). The research stage consists of data collection in a Mini Studio, image processing using ImageJ, and image classification with ANN. The automatic sorting process begins with items entering the belt, where they are processed in four phases: (1) separating good and rejects chili, (2) separating red from green chili, (3) distinguishing large and small red peppers, and (4) separating large and small green peppers. Automatic sorting and grading were based on image data processed using ANN. The best activation function was tansig-logsig-purelin with MAPE 1.220, RMSE 0.010, and R2 = 1 during training. During testing, the MAPE 0.158, RMSE 1.790, and R2 = 0.963. The criteria produced grade 1 (red, 10-15 cm), grade 2 (green, 10-15 cm), grade 3 (red, 5-9.99 cm), and reject grade. The quality of large red chilies is used as a reference for market pricing: grade 1 (IDR 60,000/kg), grade 2 (IDR 40,000/kg), and grade 3 (IDR 25.000 − 35,000). Assessing quality based on color with an automatic conveyor can reduce sorting and grading time by 70% compared to conventional methods.
Keywords: ANN, Color, Grading, Image Processing, Sorting.
References
Abubeker, K.M., Abhijit., Akhil, S., Kumar, V.K.A., & Jose, B.K. (2023). Computer vision assisted real-time bird eye chili classification using YOLO V5 framework. Journal of Artificial Intelligence and Technology, 4(3), 265-271. https://doi.org/10.37965/jait.2023.0251
Akila, K., Sabitha, B., Balamurugan, K., Balaji, K., & Gourav, A. (2019). Mechatronics system design for automated chilli segregation. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(8S), 546–550. https://www.ijitee.org/wp-content/uploads/papers/v8i8s/H10910688S19.pdf
Asian, J., Arianti, N.D., Ariefin., & Muslih, M. (2024). Comparison of feature extraction and auto-preprocessing for chili pepper (Capsicum Frutescens) quality classification using machine learning. IAES International Journal of Artificial Intelligence, 13(1), 319–328. https://doi.org/10.11591/ijai.v13.i1.pp319-328
Chen, H., Zhang, R., Peng, J., Peng, H., Hu, W., Wang, Y., & Jiang, P. (2024). YOLO-chili: An efficient lightweight network model for localization of pepper picking in complex environments. Applied Sciences, 14(13). https://doi.org/10.3390/app14135524
Getahun, E., Delele, M.A., Gabbiye, N., Fanta, S.W., & Vanierschot, M. (2021). Studying the drying characteristics and quality attributes of chili pepper at different maturity stages: Experimental and mechanistic model. Case Studies in Thermal Engineering, 26. https://doi.org/10.1016/j.csite.2021.101052
Ghuge, S., Rathore, H., Ghayal, G., Ghorpade, S., & Mangate, L. (2023). Smart segregation system for fruit ripeness. E3S Web of Conferences, 469. https://doi.org/10.1051/e3sconf/202346900008
Hadinegoro, A., & Rizaldilhi, D.A. (2021). Pengaruh HSV pada pengolahan citra untuk kematangan buah cabai. Building of Informatics, Technology and Science (BITS), 3(3), 155–163. https://doi.org/10.47065/bits.v3i3.1020
Harel, B., van Essen, R., Parmet, Y., & Edan, Y. (2020). Viewpoint analysis for maturity classification of sweet peppers. Sensors, 20(13), 1–22. https://doi.org/10.3390/s20133783
Hendrawan, Y., Rohmatulloh, B., Prakoso, I., Liana, V., Fauzy, M.R., Damayanti, R., Hermanto, M.B., Al Riza, D.F., & Sandra. (2021). Classification of large green chilli maturity using deep learning. IOP Conference Series: Earth and Environmental Science, 924, 012009. https://doi.org/10.1088/1755-1315/924/1/012009
Hetharua, A.D., Sumarno, S., Gunawan, I., Hartama, D., & Kirana, I.O. (2021 Alat penyortir buah tomat berdasarkan warna berbasis mikrokontroller arduino. Jurnal Penelitian Inovatif, 1(2), 119–130. https://doi.org/10.54082/jupin.18
Hwang, S.Y., & Kim, J.J. (2023). A universal activation function for deep learning. Computers, Materials and Continua, 75(2), 3553–3569. https://doi.org/10.32604/cmc.2023.037028
Iwan, I., Lahming, L., Jamaluddin, J., & Lestari, N. (2022). Rancang bangun mesin sortasi buah jeruk manis berdasarkan warna berbasis arduino nano. Jurnal Pendidikan Teknologi Pertanian, 8(1), 1. https://doi.org/10.26858/jptp.v8i1.19297
Khafit, M.N., Khamdi, N., Jaenudin, J., & Edilla, E. (2023). Design and development of an apple sorting machine based on size and color differences using arduino microcontroller. JTEV (Jurnal Teknik Elektro Dan Vokasional), 9(1), 147. https://doi.org/10.24036/jtev.v9i1.122935
Khan, A., Hassan, T., Shafay, M., Fahmy, I., Werghi, N., Mudigansalage, S., & Hussain, I. (2023). Tomato maturity recognition with convolutional transformers. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-50129-w
Khuriyati, N., Nugroho, D.A., & Wicaksono, N.A. (2020). Quality assessment of chilies (Capsicum annuum L.) by using a smartphone camera. IOP Conference Series: Earth and Environmental Science, 425, 012040. https://doi.org/10.1088/1755-1315/425/1/012040
Khuriyati, N., Pamungkas, A.P., & Agung, A.P. (2019). The sorting and grading of red chilli peppers (Capsicum annuum L.) using digital image processing. SSRG Seven Sense Research Group, 6(4), 17–23. https://doi.org/10.14445/23942568/IJAES-V6I4P104
Luthfi, A., Sari, A.M., Dewi, G.R., Dwijayanti, Y., Satya, T.P., Sari, A.R., & Anoraga, S.B. (2023). Determination of maturity classification and quality of large red chili (Capsicum annuum L.) using color grab application. Agrointek: Jurnal Teknologi Industri Pertanian, 17(2), 288–294.
Mohi-Alden, K., Omid, M., Firouz, M.S., & Nasiri, A. (2023). A machine vision-intelligent modelling-based technique for in-line bell pepper sorting. Information Processing in Agriculture, 10(4), 491–503. https://doi.org/10.1016/j.inpa.2022.05.003
Moya, V., Quito, A., Pilco, A., Vásconez, J. P., & Vargas, C. (2024). Crop detection and maturity classification using a YOLOv5-based image analysis. Emerging Science Journal, 8(2), 496–512. https://doi.org/10.28991/ESJ-2024-08-02-08
Prayogi, A., Pardede, A.M.H., & Lumbanbatu, K. (2022). Image processing detecting chili fruit based on maturity level using backpropagation method. International Journal of Health, Engineering and Technology, 1(3). https://doi.org/10.55227/ijhet.v1i3.63
Sari, E.I., Suhada., Anggraini, F., Hartama, D., & Kirana, I.O. (2021). Prototype alat pengecekan dan penyortir kesegaran cabai berdasarkan warna menggunakan sensor Tcs230 berbasis arduino. BEES: Bulletin of Electrical and Electronics Engineering, 2(1), 1–6. https://doi.org/10.47065/bees.v2i1.762
Shao, Y., Ji, S., Xuan, G., Ren, Y., Feng, W., Jia, H., Wang, Q., & He, S. (2024). Detection and analysis of chili pepper root rot by hyperspectral imaging technology. Agronomy, 14(1), 226. https://doi.org/10.3390/agronomy14010226
Sihombing, Y.F., Septiarini, A., Kridalaksana, A.H., & Puspitasari, N. (2022). Chili classification using shape and color features based on image processing. Scientific Journal of Informatics, 9(1), 42–50. https://doi.org/10.15294/sji.v9i1.33658
Simanungkalit, F.J., & Manurung, H. (2024). Artificial neural network model to predict °brix and ph of banana based on color parameters. Jurnal Teknik Pertanian Lampung, 13(3), 739-749. https://doi.org/10.23960/jtep-l.v13i3.739-749
Subrata, I.D.M., & Baiquni, A.D. (2024). Application of Stereo Vision to Control the Movement of the Robot Arm Towards the Position of Red Chilies. Jurnal Teknik Pertanian Lampung, 13(3), 615-627. https://doi.org/10.23960/jtep-l.v13i3.615-627
Subrata, I.D.M., Ramadhan, A.Z., & Sutejo, A. (2022). Development of cherry tomato quality detection system based on color using TCS230 optical sensor. Jurnal Ilmiah Rekayasa Pertanian dan Biosistem, 10(2), 171–183. https://doi.org/10.29303/jrpb.v10i2.389
Tang, R., Supit, I., Hutjes, R., Zhang, F., Wang, X., Chen, X., Zhang, F., & Chen, X. (2023). Modelling growth of chili pepper (Capsicum annuum L.) with the WOFOST model. Agricultural Systems, 209. https://doi.org/10.1016/j.agsy.2023.103688
Thinh, N.T., Thong, N.D., & Cong, H.T. (2020). Sorting and classification of mangoes based on artificial intelligence. International Journal of Machine Learning and Computing, 10(2), 374–380.
Tosi, R.B., Mbura, H.D., & Kaesmetan, Y.R. (2024). Implementasi CNN dalam mengidentifikasi kematangan cabai berdasarkan warna. INDOTECH Indonesian Journal of Education And Computer Science, 2(1), 34–42. https://doi.org/10.60076/indotech.v2i1.385
Zhou, Z., Zahid, U., Majeed, Y., Nisha, Mustafa, S., Sajjad, M.M., Butt, H.D., & Fu, L. (2023). Advancement in artificial intelligence for on-farm fruit sorting and transportation. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1082860
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