期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2025-11-05 |
| 作品名稱 | A Machine Learning-Based Model for Classifying the Shape of Tomato |
| 作品名稱（其他語言） | |
| 著者 | Ho, Trang-Thi; Rosdyana Mangir Irawan Kusuma; Van Lam Ho; Hsiang Yin Wen |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | AgriEngineering 7(11) , p.373 |
| 摘要 | Most fruit classification studies rely on color-based features, but shape-based analysis provides a promising alternative for distinguishing subtle variations within the same variety. Tomato shape classification is challenging due to irregular contours, variable imaging conditions, and difficulty in extracting consistent geometric features. In this study, we propose an efficient and structured workflow to address these challenges through contour-based analysis. The process begins with the application of a Mask Region-based Convolutional Neural Network (Mask R-CNN) model to accurately isolate tomatoes from the background. Subsequently, the segmented tomatoes are extracted and encoded using Elliptic Fourier Descriptors (EFDs) to capture detailed shape characteristics. These features are used to train a range of machine learning models, including Support Vector Machine (SVM), Random Forest, One-Dimensional Convolutional Neural Network (1D-CNN), and Bidirectional Encoder Representations from Transformers (BERT). Experimental results observe that the Random Forest model achieved the highest accuracy of 79.4%. This approach offers a robust, interpretable, and quantitative framework for tomato shape classification, reducing manual labor and supporting practical agricultural applications. |
| 關鍵字 | tomato shape classification; fruit contour; image classification; machine learning; Elliptic Fourier Descriptors; Mask R-CNN |
| 語言 | en_US |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128589 ) |