Full
Outline
To address the problems of high labor intensity and low efficiency in manual measurements of heading date in rice breeding, this study proposes an intelligent prediction method that integrates UAV imagery, the YOLOv10b object detection algorithm, and a random forest regression model to achieve automated and efficient prediction of heading date in rice breeding plots.
Using rice breeding plots as the research subject, UAV imagery was acquired at 15 observation time points from booting to maturity. Based on the YOLOv10b algorithm, a rice panicle detection model was trained to recognize and count panicles in plot images. Subsequently, the numbers of panicles at each observation time point were used as input variables, and the number of days from transplanting to heading was used as the prediction variable to construct and compare multiple heading date prediction models. Based on this, feature importance analysis and dimensionality reduction were performed to develop a simplified model.
Experimental results showed that the YOLOv10b model achieved a precision of 91.7%, a recall of 92.1%, and an F1 score of 0.919 in the panicle detection task. In the heading date prediction task, the random forest model constructed using the sequential features of panicle counts from all 15 observation time points achieved an R2 of 0.84, an RMSE of 1.57 days, and an MAE of 1.12 days. Furthermore, after feature dimensionality reduction based on feature importance analysis, a simplified model constructed using 9 observation time points achieved an R2 of 0.83, an RMSE of 1.65 days, and an MAE of 1.16 days. This approach effectively reduced feature dimensionality while maintaining prediction accuracy, thereby improving the model's efficiency and practicality.
The proposed method can effectively reduce the workload of manual measurements, improve the efficiency of heading date monitoring, and realize automated prediction of heading date in breeding plots. It provides a feasible data foundation and technical pathway for intelligent breeding and precision agricultural management.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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