Optimizing Quality Control: A Comprehensive Analysis of Computer Vision Methods for Assessing Vegetables and Fruits

Authors

  • Zahow Muftah Khamees University of Ajdabyia
  • Abdusalam Aboubaker Abdusalam University of Ajdabyia

DOI:

https://doi.org/10.37376/sjuob.v37i2.7133

Keywords:

Computer Vision, Classification Mechanisms, Fruits, Image Processing, Quality Assessment, Vegetables

Abstract

Efficient quality control in the agriculture sector, particularly regarding the inspection of vegetables and fruits, stands as a critical necessity in today's health-focused industry. Conventional fruit grading methods, ill-suited for large-scale production, demand an automated, non-invasive, and economically feasible substitute. Computer vision emerges as a promising avenue, leveraging image analysis and machine learning algorithms to evaluate the quality of produce. The convergence of computer vision and image processing technologies in contemporary agriculture has brought about a substantial transformation in quality assessment methodologies. This paper conducts an in-depth exploration of the amalgamation of computer vision and image processing techniques for the evaluation of agricultural produce quality. Through a comprehensive review, this scientific analysis investigates the integration of computer vision and image processing techniques in agricultural quality assessment. It scrutinizes key studies, their practical implementations, outcomes, and the research voids they reveal. Technological progressions within the agricultural domain have the potential to amplify productivity and curtail the circulation of flawed or substandard products. Moreover, this study deliberates on the forthcoming trends in computer vision technology applications, accentuating their prospective influence on the vegetables and fruits industry.

Downloads

Download data is not yet available.

Author Biographies

Zahow Muftah Khamees , University of Ajdabyia

Department of Information Technology, Faculty of Computers and Information Technology, University of Ajdabyia, Libya.

Abdusalam Aboubaker Abdusalam , University of Ajdabyia

Therapeutic Nutrition, University of Ajdabyia, Ajdabyia, Libya.

References

Bhargava A, Bansal A. Fruits and vegetables quality evaluation using computer vision: A review. J King Saud Univ - Comput Inf Sci. 2021;33(3):243-257.

https://doi.org/10.1016/j.jksuci.2018.06.002

Palumbo M, Cefola M, Pace B, Attolico G, Colelli G. Computer vision system based on conventional imaging for non-destructively evaluating quality attributes in fresh and packaged fruit and vegetables. Postharvest Biology and Technology. 2023;200:112332.

https://doi.org/10.1016/j.postharvbio.2023.112332

Satheesha KM, Rajanna KS. A Review of the Literature on Arecanut Sorting and Grading Using Computer Vision and Image Processing. International Journal of Applied Engineering and Management Letters (IJAEML). 2023;7(2):50-67.

https://doi.org/10.47992/IJAEML.2581.7000.0174

Jana S, Parekh R. Shape-based fruit recognition and classification. In: Computational Intelligence, Communications, and Business Analytics: First International Conference, CICBA 2017, Kolkata, India, March 24–25, 2017, Revised Selected Papers, Part II. Springer Singapore; 2017. pp. 184-196.

https://doi.org/10.1007/978-981-10-6430-2

Chithra PL, Henila M. Fruit classification using image processing techniques. International Journal of Computer Sciences and Engineering. 2019;7(5):131-135.

https://doi.org/10.26438/ijcse/v7si5.131135

Jana S, Parekh R, Sarkar B. An Approach Towards Classification of Fruits and Vegetables Using Fractal Analysis. In: Computational Intelligence, Communications, and Business Analytics: Second International Conference, CICBA 2018, Kalyani, India, July 27–28, 2018, Revised Selected Papers, Part II. Springer Singapore; 2019. pp. 167-180.

https://doi.org/10.1007/978-981-13-8581-0_14

Jana S, Parekh R, Sarkar B. A De novo approach for automatic volume and mass estimation of fruits and vegetables. Optik. 2020;200:163443.

https://doi.org/10.1016/j.ijleo.2019.163443‏

Bird JJ, Barnes CM, Manso LJ, Ekárt A, Faria DR. Fruit quality and defect image classification with conditional GAN data augmentation. Scientia Horticulturae. 2022;293:110684.

https://doi.org/10.1016/j.scienta.2021.110684

Wedha BY, Vasandani MS, Wedha AEPB. Embarking on Comprehensive Exploration of Classification System of Fruits and Vegetables. Sinkron: jurnal dan penelitian teknik informatika. 2023;8(4):2560-2567.

https://doi.org/10.33395/sinkron.v8i4.13041

Turaev S, Abd Almisreb A, Saleh MA. Application of transfer learning for fruits and vegetable quality assessment. In: 2020 14th International Conference on Innovations in Information Technology (IIT). IEEE; November 2020. pp. 7-12.

https://doi.org/10.1109/IIT50501.2020.9299048

Mandal JK, Mukhopadhyay S, Dutta P, Dasgupta K (Eds.). Algorithms in machine learning paradigms. Springer; 2020.

https://doi.org/10.1007/978-981-15-1041-0_7

Tapia-Mendez E, Cruz-Albarran IA, Tovar-Arriaga S, Morales-Hernandez LA. Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables. Applied Sciences. 2023;13(22):12504.

https://doi.org/10.3390/app132212504

Elhariri E, El-Bendary N, Hassanien AE, Badr A, Hussein AM, Snášel V. Random forests-based classification for crops ripeness stages. In: Proceedings of the fifth international conference on innovations in bio-inspired computing and applications IBICA 2014. Springer International Publishing; 2014. pp. 205-215.

https://doi.org/10.1007/978-3-319-08156-4_21

Bhargava A, Bansal A. Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University-Computer and Information Sciences. 2021;33(3):243-257.

https://doi.org/10.1016/j.jksuci.2018.06.002

Jana S, Parekh R. Intra-class recognition of fruits using color and texture features with neural classifiers. International Journal of Computer Applications. 2016;148(11):1-6.

https://doi.org/10.5120/ijca2016911283

Xin Q, Luo Q, Zhu H. Key Issues and Countermeasures of Machine Vision for Fruit and Vegetable Picking Robot. In: Mechatronics and Automation Technology. IOS Press; 2024. pp. 69-78.

https://doi.org/ 10.3233/ATDE231092

Elhariri E, El-Bendary N, Hussein AM, Hassanien AE, Badr A. Bell pepper ripeness classification based on support vector machine. In: 2014 International Conference on Engineering and Technology (ICET). IEEE; April 2014. pp. 1-6.

https://doi.org/10.1007/978-3-319-08156-4_21

Tao D, Yang P, Feng H. Utilization of text mining as a big data analysis tool for food science and nutrition. Comprehensive Reviews in Food Science and Food Safety. 2020;19(2):875-894.

https://doi.org/10.1111/1541-4337.12540

Appadoo A, Gopaul Y, Pudaruth S. FruVegy: An Android App for the Automatic Identification of Fruits and Vegetables using Computer Vision and Machine Learning. International Journal of Computing and Digital Systems. 2023;13(1):169-178.

http://dx.doi.org/10.12785/ijcds/130114

Zhou T, Zhan W, Xiong M. A series of methods incorporating deep learning and computer vision techniques in the study of fruit fly (Diptera: Tephritidae) regurgitation. Frontiers in Plant Science. 2024;14:1337467.

https://doi.org/10.3389/fpls.2023.1337467

Gom-os DFK. Fruit Classification using Colorized Depth Images. International Journal of Advanced Computer Science and Applications. 2023;14(5).

http://dx.doi.org/10.14569/IJACSA.2023.01405106

Asadi M, Ghasemnezhad M, Bakhshipour A, Olfati JA, Mirjalili MH. Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems. BMC Plant Biology. 2024;24(1):13.

https://doi.org/10.1186/s12870-023-04661- 6

Wang J, Zhang C, Yan T, Yang J, Lu X, Lu G, Huang B. A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation. Complex & Intelligent Systems. 2023;9(4):4227-4247.

https://doi.org/10.1186/s12870-023-04661-6

Anjali, Jena A, Bamola A, Mishra S, Jain I, Pathak N, et al. State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions. Food Production, Processing and Nutrition. 2024; 6(1):56.

https://doi.org/10.1186/s43014-023-00205-5

Beltran JK, Ibarlin DK, Mapa MI, Arboleda ER. Exploring computer vision, machine learning, and robotics applications in banana grading: A review. 2024.

https://doi.org/10.30574/ijsra.2024.11.1.0180

Elhariri E, El-Bendary N, Fouad MMM, Platoš J, Hassanien AE, Hussein AM. Multi-class SVM based classification approach for tomato ripeness. In: Innovations in Bio-inspired Computing and Applications: Proceedings of the 4th International Conference on Innovations in Bio-Inspired Computing and Applications, IBICA 2013, August 22-24, 2013-Ostrava, Czech Republic. Springer International Publishing; 2014. pp. 175-186.

https://doi.org/10.1007/978-3-319-01781-5_17

Balderas-Silva D, Lopez-Bernal D, Diaz-Larra A, Rojas M, Ponce P, Molina A. A Review on Agriculture Monitoring: Plant Disease Detection Using Computer Vision and Machine Learning. Available at SSRN 4.

http://dx.doi.org/10.2139/ssrn.4740369‏

Ni J, Gao J, Deng L, Han Z. Monitoring the change process of banana freshness by GoogLeNet. IEEE Access. 2020;8:228369-228376.

https://doi.org/10.1016/j.dib.2023.109524

Hameed K, Chai D, Rassau A. A comprehensive review of fruit and vegetable classification techniques. Image and Vision Computing. 2018;80:24.

https://doi.org/10.1016/j.imavis.2018.09.016

Khamees ZM, Abdusalam AA. A Review: Comparative Analysis of Computer Vision Techniques for Defect Detection and Categorization in Bananas and Apples. In: Sebha University Conference Proceedings. 2024;3(2):391-397

https://doi.org/10.51984/sucp.v3i2.3241

Nguyen, N. M. T., & Liou, N. S. (2022). Ripeness Evaluation of Achacha Fruit Using Hyperspectral Image Data. Agriculture, 12(12), 2145.

https://doi.org/10.3390/agriculture12122145

Downloads

Published

2024-12-26

How to Cite

Muftah Khamees , Z. ., & Aboubaker Abdusalam , A. . (2024). Optimizing Quality Control: A Comprehensive Analysis of Computer Vision Methods for Assessing Vegetables and Fruits. The Scientific Journal of University of Benghazi, 37(2), 101–114. https://doi.org/10.37376/sjuob.v37i2.7133

Issue

Section

Applied Sciences