ORANGE FRUIT IMAGE QUALITY ASSESSMENT USING HYBRID MODEL
DOI:
https://doi.org/10.36755/jac.v2i1.58Keywords:
Communication Connection, Data center, Firewall Virtual machineAbstract
Since ripeness is regarded by consumers as the most important quality indication in the crops industry, maintaining and monitoring its ripeness has become a major issue in the business of growing crops. Additionally, as it significantly affects the product's quality and consumer preferences, the product's appearance is one of the makers' most pressing worries. However, the forecasting of storage life and the selection of the best harvest dates are still largely based on individual interpretation and practical experience. Threshold segmentation and certain morphological techniques are used in the early stage of the extraction of an area of interest. The segmented orange images are then divided into training and testing data sets, and the second stage entails extracting color-based traits from them. Choosing the classifier training settings is the focus of the third phase. The final phase, which makes use of the previously trained ANN, categorizes the data. One of the most crucial conclusions of this study is that creating a neural network is an empirical process that requires a lot of trials in order to identify the ideal variables that will give the neural classification algorithm the best performance. initialization of weights, the quantity of hidden neurons. Based on the proposed model the accuracy and efficiency is 1.3% which is more accurate result.
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1. Anuja, P. K., & Paira, P. (2020). Lumi-nescent anticancer Ru (II)-arenebipyridine and phenanthroline complexes: Synthesis, characterization, DFT studies, biological interactions and cellular imaging application. In Journal of Inorganic Biochemis-try, 208(2), 111-099. DOI: https://doi.org/10.1016/j.jinorgbio.2020.111099
2. Abdulhamid, U. F., Daniel, S., & Ba-bawuro, U. (2018). Classification of Soya Beans Based Image Pro-cessing Techniques and Artificial Neu-ral Network. In Journal of Advances in Mathematics and Computer Science, 1-9. DOI: https://doi.org/10.9734/JAMCS/2018/39611
3. Al-Daour, A. F., Al-Shawwa, M. O., & Abu-Naser, S. S. (2020). Banana clas-sification using deep learning. In Inter-national Journal of Academic Infor-mation Systems Research (IJAISR), 3(12), 18-19.
4. Atul Narayan, S. P., & Palade, L. I. (2020). Comparison of a natural con-figuration approach and a structural pa-rameter approach to model the Payne effect. In Acta Mechanica, 231(11), 4781-4802. DOI: https://doi.org/10.1007/s00707-020-02774-9
5. Alfatni, M. S. M., Shariff, A. R. M., Abdullah, M. Z., Marhaban, M. H., Shafie, S. B., Bamiruddin, M. D., & Saaed, O. M. B. (2014, June). Oil palm fresh fruit bunch ripeness classification based on rule-based expert system of ROI image processing technique re-sults. In IOP Conference Series: Earth and Environmental Science 18th & 19th December Malaysia (pp. 012-018). IOP Publishing. DOI: https://doi.org/10.1088/1755-1315/20/1/012018
6. Ullah, A., Nawi, N. M., & Ouhame, S. (2022). Recent advancement in VM task allocation system for cloud com-puting: review from 2015 to2021. Artificial Intelligence Review, 1-45. DOI: https://doi.org/10.1007/s10462-021-10071-7
7. Ullah, A., & Chakir, A. (2022). Im-provement for tasks allocation system in VM for cloud datacenter using mod-ified bat algorithm. Multimedia Tools and Applications, 81(20), 29443-29457. DOI: https://doi.org/10.1007/s11042-022-12904-1
8. Sebai, D., & Shah, A. U. (2023). Se-mantic-oriented learning-based image compression by Only-Train-Once quan-tized autoencoders. Signal, Image and Video Processing, 17(1), 285-293. DOI: https://doi.org/10.1007/s11760-022-02231-1
9. Ouhame, S., Hadi, Y., & Ullah, A. (2021). An efficient forecasting ap-proach for resource utilization in cloud data center using CNN-LSTM mod-el. Neural Computing and Applica-tions, 33, 10043-10055. DOI: https://doi.org/10.1007/s00521-021-05770-9
10. Jerripothula, K. R., Shukla, S. K., Jain, S., & Singh, S. (2021, October). Fruit Maturity Recognition from Agri-cultural, Market and Automation Per-spectives. In IECON Annual Confer-ence of the IEEE Industrial Electronics Society (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/IECON48115.2021.9589215
11. Jaspin, K., Selvan, S., Rose, J. D., Ebenezer, J., & Chockalingam, A. (2021, October). Real-Time Surveil-lance for Identification of Fruits Rip-ening Stages and Vegetables Matura-tion Stages with Infection Detection. In 2021 6th International Con-ference on Signal Processing, Compu-ting and Control (ISPCC) (pp. 581-586). IEEE. DOI: https://doi.org/10.1109/ISPCC53510.2021.9609441
12. Islam, M. S., Dowla, M. Y. U., Rezaul, K. M., & Grout, V. (2020). Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning. In IET Image Processing, 14(11), 2442-2456. DOI: https://doi.org/10.1049/iet-ipr.2019.0738
13. Irhebhude, M., O Kolawole, A., & B Bugaje, F. (2021). Recognition of Ripe, Unripe and Defective Mangoes and Oranges using Image Processing with Colour and Texture Features and Locality Preserving Projection (LoPP) Tech-niques. In International Journal of Computing and Digital System, (18-19).
14. Hamza, R., & Chtourou, M. (2018). Orange ripeness estimation using artifi-cial neural network. In 2018 In-ternational Conference on High Per-formance Computing & Simulation (HPCS) July, (pp 229-234). IEEE. DOI: https://doi.org/10.1109/HPCS.2018.00049
15. Hamid, M., Usman, M., Zubair, T., Haq, R. U., & Wang, W. (2018). Shape effects of MoS2 nanoparticles on rotat-ing flow of nanofluid along a stretch-ing surface with variable thermal con-ductivity: A Galerkin approach. In in-ternational Journal of Heat and Mass Transfer, 124(1), 706-714. DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2018.03.108
16. Hadfi, I. H., & Yusoh, Z. I. M. (2018). Banana ripeness detection and serv-ing’s recommendation system using ar-tificial intelligence techniques. In Journal of Telecommunication, Elec-tronic and Computer Engineering (JTEC), 10(2-8), 83-87.
17. Goh, J. Q., Mohamed Shariff, A. R., & Mat Nawi, N. (2021). Application of Optical Spectrometer to Determine Maturity Level of Oil Palm Fresh Fruit Bunches Based on Analysis of the Front Equatorial, Front Basil, Back Equatorial, Back Basil and Apical Parts of the Oil Palm Bunches. In Agricul-ture, 11(12), 1179. DOI: https://doi.org/10.3390/agriculture11121179
18. Ghatode, P., & Sharma, S. K. (2021). The Role of Deep Learning and Deep Neural Networks in Predicting and Measurement of Quality of Orange Fruits. In SPAST Abstracts, 1(01), 5-7.
19. Fiona, M. R., Thomas, S., Maria, I. J., & Hannah, B. (2019, November). Iden-tification of ripe and unripe citrus fruits using artificial neural network. In Journal of Physics, (012-033). DOI: https://doi.org/10.1088/1742-6596/1362/1/012033
20. Elhariri, E., El-Bendary, N., Hussein, A. M., Hassanien, A. E., & Badr, A. (2014, April). Bell pepper ripeness clas-sification based on support vector ma-chine. In International Engineering and Technology (ICET), 1-6. DOI: https://doi.org/10.1109/ICEngTechnol.2014.7016802
21. Dileep Sean, Y., D Smith, D., SP Bitra, V., Bera, V., & Umar, N. (2021). De-velopment of Computer Vision System for Fruits. In Journal of Agricultural Science, 41(3), 03-11.
22. de Luna, R. G., Dadios, E. P., Banda-la, A. A., & Vicerra, R. R. P. (2019). Size Classification of Tomato Fruit Us-ing Thresholding, Machine Learning, and Deep Learning Techniques. In Journal of Agricultural Science, 41(3), 586-596 DOI: https://doi.org/10.17503/agrivita.v41i3.2435
23. Chopra, H., Singh, H., Bamrah, M. S., Mahbubani, F., Verma, A., Hooda, N... & Singh, A. K. (2021). Efficient fruit Grading System using Spectrophotom-etry and Machine Learning Approach-es. In IEEE Sensors Journal, 1-6. DOI: https://doi.org/10.1109/JSEN.2021.3075465
24. Saqib, I. (2023). Comparison Of Dif-ferent Firewalls Performance In A Vir-tual For Cloud Data Center. Journal of Advancement in Computing, 1(1), 21-28. DOI: https://doi.org/10.36755/jac.v1i1.49
25. Bongulwar, D. M. (2021). Identifica-tion of Fruits Using Deep Learning Approach. In IOP Conference Se-ries: In Materials Science and Engi-neering, 8th -10th May, Sehore (pp 012-004).
26. Bhargava, A., & Bansal, A. (2020). Automatic detection and grading of multiple fruits by machine learning. In Food Analytical Methods, 13(3), 751-761. DOI: https://doi.org/10.1007/s12161-019-01690-6
27. Behera, S. K., Rath, A. K., Mahapatra, A., & Sethy, P. K. (2020). Identifica-tion, classification & grading of orange fruit using machine learning & comput-er intelligence: a review. In Journal of Ambient Intelligence and Human-ized Computing, 1-11. DOI: https://doi.org/10.1007/s12652-020-01865-8
28. Aznaoui, H., Raghay, S., Aziz, L., & Khan, M. H. (2021). An efficient GAF routing protocol using an optimized weighted sum model in WSN. Indonesian Journal of Electrical Engineering and Computer Sci-ence, 22(1), 396-406.
29. Assoi, E. K., Bagui, O. K., Kouakou, B. K., Gbogbo, A. Y., Soro, D., Zoueu, J. T., & d’Ivoire, C. (2021). Es-timating maturity by measuring pH, sugar, dry matter, water and vitamin C content of cashew orange (Anacar-dium occidentale) from remote spectral reflectance data using neural net-work. In Journal of Crop Sci-ence, 15(7), 1029-1034. DOI: https://doi.org/10.21475/ajcs.21.15.07.p3075
30. Ding, L., Wang, Z., Wang, X., & Wu, D. (2020). Security information trans-mission algorithms for IoT based on cloud computing. Computer Commu-nications, 155, 32-39. DOI: https://doi.org/10.1016/j.comcom.2020.03.010
31. Ahmad, W., Rasool, A., Javed, A. R., Baker, T., & Jalil, Z. (2021). Cyber se-curity in IoT-based cloud computing: A comprehen-sive survey. Electronics, 11(1), 16. DOI: https://doi.org/10.3390/electronics11010016
32. Bhargava, A., & Bansal, A. (2021). Fruits and vegetables quality evalua-tion using computer vision: A re-view. Journal of King Saud University-Computer and Information Scienc-es, 33(3), 243-257. DOI: https://doi.org/10.1016/j.jksuci.2018.06.002
33. Rai, S. A., Kuan, W. L., & Mustafa, R. (2023). An Enhanced Compression Method for Medical Images Using SPIHT Encoder for Fog Compu-ting. International Journal of Image and Graphics, 2550025. DOI: https://doi.org/10.1142/S0219467825500251
34. Aznaoui, A., Raghay, S., Aziz, L., & Khan, M. H. (2021). An efficient GAF routing protocol using an optimized weighted sum model in WSN. Indonesian Journal of Electrical Engineering and Computer Sci-ence, 22(1), 396-406. DOI: https://doi.org/10.11591/ijeecs.v22.i1.pp396-406
35. Khan, S. N., Nawi, N. M., Imrona, M., Shahzad, & Rahman, A. U. (2018). Opinion mining summarization and au-tomation process: a sur-vey. International Journal on Ad-vanced Science Engineering Infor-mation Technology, 8(5), 1836-1844. DOI: https://doi.org/10.18517/ijaseit.8.5.5002
36. Dhiman, B., Kumar, Y., & Kumar, M. (2022). Fruit quality evaluation using machine learning techniques: review, motivation and future perspec-tives. Multimedia Tools and Applica-tions, 81(12), 16255-16277. DOI: https://doi.org/10.1007/s11042-022-12652-2
37. Harini, S., Deshpande, P., Dutta, J., & Rai, B. (2018, May). A Deep Learning-Based Fruit Quality Assessment Sys-tem. In International Conference on Water Energy Food and Sustainability, Cham, (pp. 187-192). Springer, DOI: https://doi.org/10.1007/978-3-030-75315-3_22
38. Hazara, M., Ghadirzadeh, A., & Kyrki, V. (2020, May). Meta reinforcement learning for sim-to-real domain adapta-tion. In IEEE (2725-2731). DOI: https://doi.org/10.1109/ICRA40945.2020.9196540
39. Chmaj, G., Sharma, S., & Selvaraj, H. (2020, August). Automated Agrono-my: Evaluation of fruits Ripeness Us-ing Machine Learning Approach. In International Conference on Sys-tems Engineering, 07 January 2021 (pp. 183-191). Springer. DOI: https://doi.org/10.1007/978-3-030-65796-3_17
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Copyright (c) 2025 Ubaid Ullah, Ali Hussain, Usman Ali , Mohabbat Ali (Author)
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