Rice Science ›› 2023, Vol. 30 ›› Issue (3): 247-256.DOI: 10.1016/j.rsci.2023.03.008
• Research Paper • Previous Articles Next Articles
Zhou Longfei1, Meng Ran1,4(), Yu Xing2, Liao Yigui1, Huang Zehua1, Lü Zhengang1, Xu Binyuan1, Yang Guodong2, Peng Shaobing2, Xu Le2,3(
)
Received:
2022-08-20
Accepted:
2022-11-30
Online:
2023-05-28
Published:
2023-03-13
Contact:
Meng Ran (mengran@mail.hzau.edu.cn); Xu Le (Le.Xu@mail.hzau.edu.cn)
Zhou Longfei, Meng Ran, Yu Xing, Liao Yigui, Huang Zehua, Lü Zhengang, Xu Binyuan, Yang Guodong, Peng Shaobing, Xu Le. Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method[J]. Rice Science, 2023, 30(3): 247-256.
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Fig. 2. Determination coefficients of vegetation indices (VIs) (A and B) and texture feature (C and D) for yield prediction of ratoon rice yield at different stubble heights. PI, Panicle initiation; HD, Heading; EF, Early filling; LF, Late filling; All represents all stubble height; SH0, SH15, SH30 and SH45 represent stubble heights of 0, 15, 30 and 45 cm, respectively.RECI, Red-edge chlorophyll index; WDRVI, Wide dynamic range vegetation index; R_ENT, Red_entropy; RE_SEC, Red-edge_second moment.
Stage | Feature | No. of features | R2 | RMSE | RRMSE |
---|---|---|---|---|---|
Panicle initiation | VIs | 2 | 0.611 | 0.459 | 0.115 |
Tex | 2 | 0.576 | 0.472 | 0.118 | |
VIs + API | 3 | 0.685 | 0.417 | 0.104 | |
Tex + API | 3 | 0.564 | 0.477 | 0.119 | |
VIs & Tex | 4 | 0.700 | 0.412 | 0.103 | |
VIs & Tex + API | 5 | 0.732 | 0.406 | 0.101 | |
Heading | VIs | 2 | 0.618 | 0.334 | 0.083 |
Tex | 1 | 0.603 | 0.341 | 0.085 | |
VIs + API | 3 | 0.654 | 0.317 | 0.079 | |
Tex + API | 3 | 0.587 | 0.349 | 0.087 | |
VIs & Tex | 3 | 0.690 | 0.302 | 0.075 | |
VIs & Tex + API | 4 | 0.705 | 0.295 | 0.073 | |
Early filling | VIs | 2 | 0.491 | 0.480 | 0.118 |
Tex | 2 | 0.582 | 0.432 | 0.107 | |
VIs + API | 3 | 0.478 | 0.486 | 0.120 | |
Tex + API | 3 | 0.599 | 0.424 | 0.105 | |
VIs & Tex | 4 | 0.606 | 0.420 | 0.104 | |
VIs & Tex + API | 5 | 0.611 | 0.417 | 0.103 | |
Late filling | VIs | 1 | 0.006 | 0.776 | 0.190 |
Tex | 1 | 0.365 | 0.551 | 0.134 | |
VIs + API | 2 | 0.257 | 0.597 | 0.146 | |
Tex + API | 2 | 0.422 | 0.523 | 0.128 | |
VIs & Tex | 2 | 0.244 | 0.606 | 0.148 | |
VIs & Tex + API | 3 | 0.502 | 0.499 | 0.122 | |
Multi-temporal | VIs | 2 | 0.780 | 0.296 | 0.072 |
Tex | 3 | 0.702 | 0.345 | 0.084 | |
VIs + API | 3 | 0.768 | 0.304 | 0.074 | |
Tex + API | 4 | 0.730 | 0.334 | 0.081 | |
VIs & Tex | 5 | 0.795 | 0.298 | 0.072 | |
VIs & Tex + API | 6 | 0.794 | 0.300 | 0.073 |
Table 1. Validation of ratoon rice yield models based on different feature sets.
Stage | Feature | No. of features | R2 | RMSE | RRMSE |
---|---|---|---|---|---|
Panicle initiation | VIs | 2 | 0.611 | 0.459 | 0.115 |
Tex | 2 | 0.576 | 0.472 | 0.118 | |
VIs + API | 3 | 0.685 | 0.417 | 0.104 | |
Tex + API | 3 | 0.564 | 0.477 | 0.119 | |
VIs & Tex | 4 | 0.700 | 0.412 | 0.103 | |
VIs & Tex + API | 5 | 0.732 | 0.406 | 0.101 | |
Heading | VIs | 2 | 0.618 | 0.334 | 0.083 |
Tex | 1 | 0.603 | 0.341 | 0.085 | |
VIs + API | 3 | 0.654 | 0.317 | 0.079 | |
Tex + API | 3 | 0.587 | 0.349 | 0.087 | |
VIs & Tex | 3 | 0.690 | 0.302 | 0.075 | |
VIs & Tex + API | 4 | 0.705 | 0.295 | 0.073 | |
Early filling | VIs | 2 | 0.491 | 0.480 | 0.118 |
Tex | 2 | 0.582 | 0.432 | 0.107 | |
VIs + API | 3 | 0.478 | 0.486 | 0.120 | |
Tex + API | 3 | 0.599 | 0.424 | 0.105 | |
VIs & Tex | 4 | 0.606 | 0.420 | 0.104 | |
VIs & Tex + API | 5 | 0.611 | 0.417 | 0.103 | |
Late filling | VIs | 1 | 0.006 | 0.776 | 0.190 |
Tex | 1 | 0.365 | 0.551 | 0.134 | |
VIs + API | 2 | 0.257 | 0.597 | 0.146 | |
Tex + API | 2 | 0.422 | 0.523 | 0.128 | |
VIs & Tex | 2 | 0.244 | 0.606 | 0.148 | |
VIs & Tex + API | 3 | 0.502 | 0.499 | 0.122 | |
Multi-temporal | VIs | 2 | 0.780 | 0.296 | 0.072 |
Tex | 3 | 0.702 | 0.345 | 0.084 | |
VIs + API | 3 | 0.768 | 0.304 | 0.074 | |
Tex + API | 4 | 0.730 | 0.334 | 0.081 | |
VIs & Tex | 5 | 0.795 | 0.298 | 0.072 | |
VIs & Tex + API | 6 | 0.794 | 0.300 | 0.073 |
Fig. 3. Validated scatter plot of measured and predicted yields of ratoon rice. A-D, Optimal prediction results for growth period at panicle initiation (A), heading (B), early filling (C) and late filling (D). E and F, Yield prediction results for the best multi-phase model without (E) and with API (F).VIs, Vegetation indices; Tex, Texture; API, Agronomic practice information; R2, Coefficient of determination; RMSE, Root mean square error; RRMSE, Relative root mean square error.
Fig. 4. Ratoon rice yield prediction map based on multi-temporal model (A), actual measured yield (B) and yield error between prediction and actual measured yields (C).
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