Improved Yield Prediction of Ratoon Rice Using Unmanned Aerial Vehicle-Based Multi-Temporal Feature Method

doi:10.1016/j.rsci.2023.03.008

Abstract

Abstract:

Pre-harvest yield prediction of ratoon rice is critical for guiding crop interventions in precision agriculture. However, the unique agronomic practice (i.e., varied stubble height treatment) in rice ratooning could lead to inconsistent rice phenology, which had a significant impact on yield prediction of ratoon rice. Multi-temporal unmanned aerial vehicle (UAV)-based remote sensing can likely monitor ratoon rice productivity and reflect maximum yield potential across growing seasons for improving the yield prediction compared with previous methods. Thus, in this study, we explored the performance of combination of agronomic practice information (API) and single-phase, multi-spectral features [vegetation indices (VIs) and texture (Tex) features] in predicting ratoon rice yield, and developed a new UAV-based method to retrieve yield formation process by using multi-temporal features which were effective in improving yield forecasting accuracy of ratoon rice. The results showed that the integrated use of VIs, Tex and API (VIs & Tex + API) improved the accuracy of yield prediction than single-phase UAV imagery-based feature, with the panicle initiation stage being the best period for yield prediction (R²as 0.732, RMSE as 0.406, RRMSE as 0.101). More importantly, compared with previous multi-temporal UAV-based methods, our proposed multi-temporal method (multi-temporal model VIs & Tex: R²as 0.795, RMSE as 0.298, RRMSE as 0.072) can increase R² by 0.020-0.111 and decrease RMSE by 0.020-0.080 in crop yield forecasting. This study provides an effective method for accurate pre-harvest yield prediction of ratoon rice in precision agriculture, which is of great significance to take timely means for ensuring ratoon rice production and food security.

Key words: ratoon rice, yield prediction, unmanned aerial vehicle, multi-temporal feature, agronomic practice, stubble height

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.

Figures/Tables 5

Fig. 1. Yield differences of ratoon rice at different stubble height groups. The red star represents the mean value for each group.

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.

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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