Automated rubber seed ventral surface identification using hue, saturation, value (HSV) image processing and decision rule approach (Record no. 74900)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02141nam a22001937a 4500 |
| 100 ## - MAIN ENTRY--AUTHOR NAME | |
| Personal name | Johari, S.N.A.M |
| 245 ## - TITLE STATEMENT | |
| Title | Automated rubber seed ventral surface identification using hue, saturation, value (HSV) image processing and decision rule approach |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher | Journal of Rubber Research, 25(3): 173-186. |
| Year of publication | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Other physical details | August |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Rubber seeds should be planted and handled correctly to boost the germination rate by placing the ventral surface facing down and adhering to the soil. Traditionally, this planting technique has been performed manually by labourers. Automation is not only the key to solving labour shortage issues but can also improve the production performance. Hence, this study was conducted to identify the dorsal and ventral surface of rubber seeds using image processing techniques of hue, saturation, value colour space and a decision rule approach. Five features were extracted at the centre of the seed based on the detected edge images, namely maximum length, ratio of major and minor axis, number of pixels, maximum convolution and number of intersections. These features were used as a dataset to develop new prediction models using a decision rule and an artificial neural network (ANN). Based on the results, it was found that the decision rule model performed better with a higher value of accuracy (88.75%), sensitivity (90%) and specificity (87.50%) compared to ANN. This was most likely due to the rules prepared by applying expert knowledge when developing a decision rule model. On the other hand, the development of the prediction model was created based on the analysis of each feature. This study could benefit the rubber industry, especially for the nursery application during the planting process, where it can potentially reduce time and labour intensity while increasing production efficiency at the same time. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Rubber seeds |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Image processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Edge detection |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Decision rule |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical Term | Artificial neural network |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Khairunniza-Bejo, S |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/s42464-022-00155-6 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journals |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Date acquired | Koha item type |
|---|---|---|---|---|---|---|---|
| RRII Library | RRII Library | 20/12/2022 | Journals |