Banana quality classification using lightweight CNN model with microservice integration system
| dc.contributor.author | Chaowalittawin, Vasutorn | |
| dc.contributor.author | Krungseanmuang, Woranidtha | |
| dc.contributor.author | Sathaporn, Posathip | |
| dc.contributor.author | Morita, Fuka | |
| dc.contributor.author | Archevapanich, Tuanjai | |
| dc.contributor.author | Purahong, Boonchana | |
| dc.date.accessioned | 2026-08-06T10:51:31Z | |
| dc.date.available | 2026-08-06T10:51:31Z | |
| dc.date.issued | 2025-06-10 | |
| dc.description.abstract | Banana sorting has been performed manually, which often leads to human error due to the high volume and diverse characteristics involved. This paper presents a banana quality classification system using ConsolutechMobileNetV2 (CST-MobileNetV2) to classify banana ripeness into four categories unripe, ripe, overripe, and rotten. A lightweight deep learning model is proposed and integrated with a uniquely designed microservice system to optimize performance while minimizing computational demands. A publicly available dataset containing 13,478 images was used, and the data split into 56% for training, 14% for validation, and 30% for testing. Image normalization and augmentation techniques were applied to enhance the model's robustness. The model's performance was evaluated using a confusion matrix, achieving 98% precision, recall, and F1-score. The proposed model was compared with other deep learning models to benchmark its performance and deployed in different operating systems to evaluate its flexibility and capabilities. The LINE platform was employed as the user interface, enabling practical interaction with users. The system also demonstrated an average response time of 9.25 seconds per image, ensuring efficient processing, delivers high accuracy and scalability making it a practical and efficient solution for automated banana quality classification. | |
| dc.identifier.citation | Engineering and Applied Science Research, 52(4), 430-438, 2025 | |
| dc.identifier.doi | 10.14456/easr.2025.38 | |
| dc.identifier.issn | 25396161 | |
| dc.identifier.other | 2-s2.0-105012779104 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/17070 | |
| dc.source | Engineering and Applied Science Research | |
| dc.subject | Banana | |
| dc.subject | CNN | |
| dc.subject | CST-MobileNetV2 | |
| dc.subject | Lightweight deep learning model | |
| dc.subject | Microservice architecture | |
| dc.title | Banana quality classification using lightweight CNN model with microservice integration system | |
| dc.type | Article |