FruitTech: A Cloud-Based Fruits Grading Machine Using Convolutional Neural Network
Keywords:
Agricultural Automation, Cloud-Based System, Convolutional Neural Networks, Fruit Grading, Fruit Quality Assessment, Machine Learning, Smart FarmingAbstract
Ensuring fruit quality is essential for farmers, especially for rambutan and calamansi, two widely grown fruits in the Philippines. However, traditional manual sorting methods are often inconsistent, time-consuming, and prone to errors, leading to post-harvest losses. This study developed FruitTech: A Cloud-Based Fruit Grading Machine, which uses Convolutional Neural Networks (CNNs) to classify fruits based on their appearance and quality automatically. The system was built using an Agile development approach, combining machine learning, cloud computing, and hardware automation. A CNN model was trained to analyze fruit images, identifying characteristics like ripeness, size, and defects. The system also includes real-time sorting mechanisms, SMS notifications, and data visualization tools, giving farmers instant access to grading results. Testing was conducted through alpha and beta phases, followed by an evaluation using ISO/IEC 25010 and UTAUT to assess system performance and user acceptance. Results showed that FruitTech significantly improved accuracy, efficiency, and ease of fruit grading, reducing the need for manual labor while providing farmers with a more reliable and accessible solution. Users responded positively to the system’s functional suitability, security, and usability, confirming its potential for real-world agricultural applications. For future improvements, researchers could expand the system to grade other fruit types, enhance image analysis in different lighting conditions, and integrate IoT or hyperspectral imaging for detecting internal defects. Developing an offline and portable version would also make FruitTech more accessible to farmers in remote areas.
References
Chen, Y.-D., Liu, X.-L., & Liu, H.-Y. (2022). Image quality predictor with highly efficient fully convolutional neural network. Advances in Multimedia, 2022, 1–12. https://doi.org/10.1155/2022/1686298
Joshi, B., Konda, B., & Karmacharya, R. (2024). Data-driven smart farming to grade and classify tomatoes using CNN and FFNN for agricultural innovation. SXC Journal, 1(1), 80–91. https://doi.org/10.3126/sxcj.v1i1.70879
Kumari, N., Rajpurohit, V. S., & Kautish, S. (2019). A study on technology-led solutions for fruit grading to address post-harvest handling issues of horticultural crops. In Advances in Environmental Engineering and Green Technologies (pp. 203–221). IGI Global. https://doi.org/10.4018/978-1-5225-9632-5.ch009
Mango fruit defect detection using MobileNetV2. (2024, May 25). IEEE Conference Publication.
Mushiri, T., & Tende, L. (2019). Automated grading of tomatoes using artificial intelligence. In Advances in Computational Intelligence and Robotics (pp. 216–239). IGI Global. https://doi.org/10.4018/978-1-5225-9687-5.ch008
Nithya, R., Santhi, B., Manikandan, R., Rahimi, M., & Gandomi, A. H. (2022). Computer vision system for mango fruit defect detection using deep convolutional neural network. Foods, 11(21), 3483. https://doi.org/10.3390/foods11213483
Setiawan, F. B., Adipradana, C. B., & Pratomo, L. H. (2023). Fruit ripeness classification system using convolutional neural network (CNN) method. PROtek Jurnal Ilmiah Teknik Elektro, 10(1), 46. https://doi.org/10.33387/protk.v10i1.5549
Sharma, K. V., Mastan, M., Jose, G. J. A., & Al-Nuaimy, L. H. (2024). Quality assessment of South Indian cherry tomatoes based on deep and convolutional neural networks. Iraqi Journal of Science, 65(10), 5754–5769. https://doi.org/10.24996/ijs.2024.65.10.35
Tang, Y., Gao, S., Zhuang, J., Hou, C., He, Y., Chu, X., Miao, A., & Luo, S. (2020). Apple bruise grading using piecewise nonlinear curve fitting for hyperspectral imaging data. IEEE Access, 8, 147494–147506. https://doi.org/10.1109/access.2020.3015808
Villaruz, J. A., Salido, J. A. A., Barrios, D. M., II, & Felizardo, R. L. (2022). Image-based recognition of fruit-bearing rambutan (Nephelium lappaceum) using deep learning. In 2022 2nd International Conference in Information and Computing Research (iCORE) (pp. 297–301). IEEE. https://doi.org/10.1109/icore58172.2022.00070
Vizvary, L., Sopiak, D., Oravec, M., & Bukovcikova, Z. (2019). Image quality detection using the Siamese convolutional neural network. In 2019 International Symposium ELMAR (pp. 109–112). IEEE. https://doi.org/10.1109/elmar.2019.8918678
Yang, Y. (2023). Fruit image classification using convolution neural networks. Highlights in Science, Engineering and Technology, 34, 110–119. https://doi.org/10.54097/hset.v34i.5430
Zheng, B., & Huang, T. (2021). Mango grading system based on optimized convolutional neural network. Mathematical Problems in Engineering, 2021, 1–11. https://doi.org/10.1155/2021/2652487
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 J. Ritualo, M. Alday, N. Olarte, N. Magnaye
This work is licensed under a Creative Commons Attribution 4.0 International License.
