Real-Time Visual Identification System to Assess Maturity, Size, and Defects in Dragon Fruits †
Abstract
1. Introduction
2. Literature Review
2.1. Dragon Fruit Cultivation
2.2. CNN
2.3. Algorithms for Image Processing
2.4. Size, Maturity, and Defects
3. Methodology
3.1. Research Framework
3.2. System Components
3.3. Design of the GUI
3.4. Image Processing
4. Results and Discussions
4.1. Evaluation Comparison
4.2. Statistical Analysis
5. Conclusions and Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Lozano, M.O.B.; Ricasio, S.E.; Valiente, L.D. Real-time Visual Identification of Defect, Size, Maturity, and Quality on Ladies’ Finger, Bitter Gourd, and Cucumber Using Image Processing and MobileNetV2. In Proceedings of the 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA), Surabaya, Indonesia, 14–15 November 2023; pp. 650–655. [Google Scholar] [CrossRef]
- Hsu, C.W.; Huang, Y.H.; Huang, N.F. Real-Time Dragonfruit’s Ripeness Classification System with Edge Computing Based on Convolution Neural Network. In Proceedings of the 2022 International Conference on Information Networking, Jeju-si, Republic of Korea, 12–15 January 2022; pp. 177–182. [Google Scholar] [CrossRef]
- Valiente, L.D.; Parco, K.M.R.; Sangalang, G.C.P. Non-destructive Image Processing Analysis for Defect Identification and Maturity Detection on Avocado Fruit. In Proceedings of the 2021 5th International Conference on Communication and Information Systems (ICCIS), Chongqing, China, 15–17 October 2021; pp. 175–179. [Google Scholar] [CrossRef]
- Dong, W.; Xia, Y.; Liu, Y. Dragon Fruit Disease Image Segmentation Based on FCM Algorithm and Two-Dimensional OTSU Algorithm. In Proceedings of the 2020 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, China, 28–30 July 2020; pp. 969–973. [Google Scholar] [CrossRef]
- Rodeo, A.J.; Castro, A.; Esguerra, E. Postharvest handling of dragon fruit (Hylocereus spp.) in the Philippines. In Proceedings of the Dragon Fruit Regional Network Initiation Workshop and Steering Committee Meeting, Taipei and Taichung, Taiwan, 1 May 2018; pp. 1–7. Available online: https://www.researchgate.net/publication/325191316 (accessed on 17 October 2024).
- (Bureau of Fisheries and Aquatic Resources) BFAR and P. Standards. Philippine National Standard. 2003. FFTC Agricultural Policy Platform. Drying Systems for Tropical Fruits. Available online: https://ap.fftc.org.tw/article/1600 (accessed on 24 April 2025).
- SKY ENGINE AI. What Is EfficientNet? 2023. Available online: https://www.skyengine.ai/blog/what-is-efficientnet (accessed on 29 April 2025).
- Wang, C.-Y.; Bochkovskiy, A.; Liao, H.-Y.M. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 17–24 June 2023; pp. 7464–7475. [Google Scholar] [CrossRef]
- Koirala, A.; Walsh, K.B.; Wang, Z.; McCarthy, C. Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of ‘MangoYOLO’. Precis. Agric. 2019, 20, 1107–1135. [Google Scholar] [CrossRef]
- Trieu, N.M.; Thinh, N.T. Quality classification of dragon fruits based on external performance using a convolutional neural network. Appl. Sci. 2021, 11, 10558. [Google Scholar] [CrossRef]
- Murzova, A.; Seth, S. Otsu’s Thresholding with OpenCV. 2020. Available online: https://learnopencv.com/otsu-thresholding-with-opencv/ (accessed on 19 October 2024).
- Buhl, N. Image Thresholding in Image Processing. 2023. Available online: https://encord.com/blog/image-thresholding-image-processing/ (accessed on 18 October 2024).
- Estellena, N.T. Dragon Fruit (Pitaya) Production Guide. Available online: https://businessdiary.com.ph/7595/dragon-fruit-pitaya-production-guide/ (accessed on 24 April 2025).
- Khatun, T.; Nirob, M.A.S.; Bishshash, P.; Akter, M.; Uddin, M.S. A comprehensive dragon fruit image dataset for detecting the maturity and quality grading of dragon fruit. Data Br. 2024, 52, 109936. [Google Scholar] [CrossRef] [PubMed]
- Choi, T.; Would, O.; Salazar-Gomez, A.; Cielniak, G. Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. In Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 23–27 May 2022; pp. 2266–2272. [Google Scholar] [CrossRef]
- Kripa, S.; Jeyalakshmi, V. Attribute-based Maturity Grading of Mango Fruit by Machine Learning. In Proceedings of the 2022 International Conference on Intelligent Innovations in Engineering and Technology (ICIIET), Coimbatore, India, 22–24 September 2022; pp. 329–334. [Google Scholar] [CrossRef]
- Shakil, R.; Islam, S.; Shohan, Y.A.; Mia, A.; Rajbongshi, A.; Rahman, M.H.; Akter, B. Addressing agricultural challenges: An identification of best feature selection technique for dragon fruit disease recognition. Array 2023, 20, 100326. [Google Scholar] [CrossRef]
Evaluation of Dragon Fruits | |||
---|---|---|---|
Fruit Number/Trial | Prototype’s Evaluation | ||
Size | Maturity | Defects | |
1 | Small | Mature | No presence of defects |
2 | Unknown | Immature | Presence of Defects |
3 | Small | Mature | No presence of defects |
4 | Unknown | Immature | Presence of Defects |
5 | Large | Mature | No presence of defects |
6 | Large | Mature | No presence of defects |
7 | Medium | Mature | No presence of defects |
8 | Unknown | Immature | No presence of defects |
9 | Medium | Mature | No presence of defects |
10 | Small | Mature | No presence of defects |
11 | Medium | Mature | Presence of Defects |
12 | Medium | Mature | Presence of Defects |
13 | Large | Mature | Presence of Defects |
14 | Medium | Mature | No presence of defects |
15 | Large | Mature | No presence of defects |
16 | Unknown | Immature | No presence of defects |
17 | Medium | Mature | Presence of Defects |
18 | Medium | Mature | Presence of Defects |
19 | Unknown | Immature | No presence of defects |
20 | Unknown | Immature | Presence of Defects |
21 | Medium | Mature | No presence of defects |
22 | Unknown | Immature | Presence of Defects |
23 | Unknown | Immature | Presence of Defects |
24 | Unknown | Immature | Presence of Defects |
25 | Unknown | Immature | Presence of Defects |
26 | Unknown | Immature | No presence of defects |
27 | Unknown | Immature | Presence of Defects |
28 | Unknown | Immature | No presence of defects |
29 | Unknown | Immature | No presence of defects |
30 | Unknown | Immature | No presence of defects |
Evaluation of Dragon Fruits | |||
---|---|---|---|
Fruit Number/Trial | Farmer’s Evaluation | ||
Size | Maturity | Defects | |
1 | Small | Mature | No presence of defects |
2 | Medium | Immature | Presence of Defects |
3 | Small | Mature | No presence of defects |
4 | Medium | Immature | No presence of defects |
5 | Large | Mature | No presence of defects |
6 | Large | Mature | No presence of defects |
7 | Medium | Mature | No presence of defects |
8 | Small | Mature | No presence of defects |
9 | Medium | Mature | No presence of defects |
10 | Small | Mature | No presence of defects |
11 | Medium | Mature | Presence of Defects |
12 | Medium | Mature | Presence of Defects |
13 | Large | Mature | Presence of Defects |
14 | Large | Mature | No presence of defects |
15 | Large | Mature | No presence of defects |
16 | Medium | Immature | No presence of defects |
17 | Large | Mature | Presence of Defects |
18 | Medium | Mature | Presence of Defects |
19 | Medium | Immature | No presence of defects |
20 | Large | Immature | Presence of Defects |
21 | Medium | Immature | No presence of defects |
22 | Small | Immature | Presence of Defects |
23 | Medium | Immature | Presence of Defects |
24 | Medium | Immature | Presence of Defects |
25 | Medium | Immature | Presence of Defects |
26 | Small | Immature | No presence of defects |
27 | Large | Immature | Presence of Defects |
28 | Medium | Immature | No presence of defects |
29 | Small | Immature | No presence of defects |
30 | Medium | Immature | No presence of defects |
Parameters | Classification | Total Number of Fruits (TF) | Total Number of Fruits Correctly Classified by the System (TC) | Accuracy |
---|---|---|---|---|
Maturity | Mature | 15 | 14 | 93.33% |
Immature | 15 | 14 | ||
Size | Small | 7 | 7 | 93.33% |
Medium | 15 | 14 | ||
Large | 8 | 7 | ||
Defect | Presence of Defects | 12 | 12 | 96.67% |
No presence of defects | 18 | 17 | ||
Overall Matching of Characteristics | Matched | 30 | 25 | 83.33% |
Not Matched | 30 | 5 | 16.67% |
Classification Category | Accuracy |
---|---|
Size | 93.33% |
Maturity | 93.33% |
Defects | 96.67% |
Overall | 83.33% |
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Cometa, L.M.A.; Garcia, R.K.T.; Latina, M.A.E. Real-Time Visual Identification System to Assess Maturity, Size, and Defects in Dragon Fruits. Eng. Proc. 2025, 92, 39. https://doi.org/10.3390/engproc2025092039
Cometa LMA, Garcia RKT, Latina MAE. Real-Time Visual Identification System to Assess Maturity, Size, and Defects in Dragon Fruits. Engineering Proceedings. 2025; 92(1):39. https://doi.org/10.3390/engproc2025092039
Chicago/Turabian StyleCometa, Lambert Marc A., Robert Kobe T. Garcia, and Mary Ann E. Latina. 2025. "Real-Time Visual Identification System to Assess Maturity, Size, and Defects in Dragon Fruits" Engineering Proceedings 92, no. 1: 39. https://doi.org/10.3390/engproc2025092039
APA StyleCometa, L. M. A., Garcia, R. K. T., & Latina, M. A. E. (2025). Real-Time Visual Identification System to Assess Maturity, Size, and Defects in Dragon Fruits. Engineering Proceedings, 92(1), 39. https://doi.org/10.3390/engproc2025092039