Method for Tea Leaf Plucking Timing Prediction with High Resolution of Images Based on YOLO11
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| Veröffentlicht in: | International Journal of Advanced Computer Science and Applications vol. 16, no. 6 (2025) |
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Science and Information (SAI) Organization Limited
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| 001 | 3231644716 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160616 |2 doi | |
| 035 | |a 3231644716 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Method for Tea Leaf Plucking Timing Prediction with High Resolution of Images Based on YOLO11 | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a As a method for estimating the time when tea leaves reach their peak quality (amino acid content) (optimum picking time), our previous study revealed that the optimum picking time is when the accumulated temperature from the detection of germination of new buds reaches 600°C. However, the accuracy of this germination detection was insufficient, so the estimation accuracy of the optimum picking time was also insufficient. Since annotation accuracy is extremely important for germination detection by YOLO11, strict attention is paid to annotation by hand and by increasing the number of training datasets. The detection accuracy has been improved compared to the germination detection by YOLOv8, which was previously proposed and used relatively low-resolution images. The conclusion of this study is that the estimation method of the optimum picking time based on the criterion that the optimum picking time (amino acid content reaches its peak) is effective when the accumulated temperature from germination detection meets the condition of 600°C. The effectiveness of this method has been confirmed by comparison with germination detection by experts. For tea farmers, being able to predict the optimum picking time, when the amino acid content in the new buds is at its peak, is important, and we are sure it will have a positive impact on agricultural researchers studying this subject. | |
| 651 | 4 | |a China | |
| 653 | |a Germination | ||
| 653 | |a Amino acids | ||
| 653 | |a Tea | ||
| 653 | |a Image resolution | ||
| 653 | |a Annotations | ||
| 653 | |a Estimation | ||
| 653 | |a Effectiveness | ||
| 653 | |a Growth models | ||
| 653 | |a Accuracy | ||
| 653 | |a Agricultural production | ||
| 653 | |a Computer science | ||
| 653 | |a Profits | ||
| 653 | |a Seeds | ||
| 653 | |a Leaves | ||
| 653 | |a Phenology | ||
| 653 | |a Germplasm | ||
| 653 | |a Gross income | ||
| 653 | |a Genetic diversity | ||
| 653 | |a Profitability | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 6 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3231644716/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3231644716/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |