Rice Growth Monitoring and Variable-Rate Fertilization Decision-Making Based on UAV and Satellite Imagery.
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| Title: | Rice Growth Monitoring and Variable-Rate Fertilization Decision-Making Based on UAV and Satellite Imagery. |
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| Authors: | Xu, Honggang1 (AUTHOR), Li, Xuehan2 (AUTHOR), Shen, Jia3 (AUTHOR), Li, Ziyi1,4 (AUTHOR), Li, Yiming1,4 (AUTHOR), Nie, Pengcheng1,2 (AUTHOR) pcn@zju.edu.cn |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1930. 22p. |
| Subjects: | Drone aircraft, Fertilizer application, Machine learning, Plant phenology, Crop growth, Precision farming, Remote-sensing images, Biomass estimation |
| Abstract: | Highlights: What are the main findings? The enhanced Completely Fair Scheduler algorithm enables automated feature selection. Integration of unmanned aerial vehicle and satellite data facilitates rapid and high-precision inversion of rice above-ground biomass. Use of crop growth status maps to formulate topdressing prescriptions for key growth stages. What are the implications of the main findings? The proposed UAV–satellite synergistic inversion framework effectively combines the high accuracy of UAV with the broad coverage advantages of satellites, providing a low-cost, precise, and efficient technical solution for regional crop monitoring. This study provides an objective basis for agricultural departments to assess crop growth, formulate fertilization plans, and predict yields. It offers data-driven support for the development of intensive, large-scale modern agriculture, aiding new types of agricultural business entities in achieving scientific and refined management. Above-ground biomass (AGB) is a critical indicator for evaluating crop growth, with its large-scale monitoring being fundamental to precision agriculture. To improve the efficiency and reduce the cost of large-scale farmland monitoring, this study developed an unmanned aerial vehicle (UAV)–satellite collaborative inversion framework. The data, including rice AGB, UAV imagery, and satellite imagery, were collected in 2024. The proposed Distance-Correlation–Correlation-Feature-Selection (DC-CFS) algorithm was employed to select compact feature subsets for each growth stage. Subsequently, six machine learning models were compared to identify the optimal UAV-scale inversion model for each specific stage. Then, the AGB values simulated by the UAV-scale model were used to train the satellite-scale inversion model. A paddy field mask covering the entire district was generated using Segment Anything Model (SAM) and the temporal spectral variation pattern of rice, enabling county-scale AGB mapping. Research results indicate that the DC-CFS algorithm can effectively select a small number of low-redundancy features for each growth stage. The optimal UAV scale model type varies dynamically with growth stages, with ExtraTrees demonstrating overall superior performance. Except for the heading stage, the R2 of the models remained above 0.69. Furthermore, the BayesianRidge algorithm also presents a viable and competitive alternative when computational efficiency is a consideration. At the satellite scale, eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees) were identified as the optimal models for rice AGB estimation due to their stable performance across all stages, with R2 values consistently above 0.74. Finally, rice growth classification maps and corresponding fertilization recommendations were generated based on the satellite-scale inversion results, providing technical support for precision agriculture practices. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The enhanced Completely Fair Scheduler algorithm enables automated feature selection. Integration of unmanned aerial vehicle and satellite data facilitates rapid and high-precision inversion of rice above-ground biomass. Use of crop growth status maps to formulate topdressing prescriptions for key growth stages. What are the implications of the main findings? The proposed UAV–satellite synergistic inversion framework effectively combines the high accuracy of UAV with the broad coverage advantages of satellites, providing a low-cost, precise, and efficient technical solution for regional crop monitoring. This study provides an objective basis for agricultural departments to assess crop growth, formulate fertilization plans, and predict yields. It offers data-driven support for the development of intensive, large-scale modern agriculture, aiding new types of agricultural business entities in achieving scientific and refined management. Above-ground biomass (AGB) is a critical indicator for evaluating crop growth, with its large-scale monitoring being fundamental to precision agriculture. To improve the efficiency and reduce the cost of large-scale farmland monitoring, this study developed an unmanned aerial vehicle (UAV)–satellite collaborative inversion framework. The data, including rice AGB, UAV imagery, and satellite imagery, were collected in 2024. The proposed Distance-Correlation–Correlation-Feature-Selection (DC-CFS) algorithm was employed to select compact feature subsets for each growth stage. Subsequently, six machine learning models were compared to identify the optimal UAV-scale inversion model for each specific stage. Then, the AGB values simulated by the UAV-scale model were used to train the satellite-scale inversion model. A paddy field mask covering the entire district was generated using Segment Anything Model (SAM) and the temporal spectral variation pattern of rice, enabling county-scale AGB mapping. Research results indicate that the DC-CFS algorithm can effectively select a small number of low-redundancy features for each growth stage. The optimal UAV scale model type varies dynamically with growth stages, with ExtraTrees demonstrating overall superior performance. Except for the heading stage, the R2 of the models remained above 0.69. Furthermore, the BayesianRidge algorithm also presents a viable and competitive alternative when computational efficiency is a consideration. At the satellite scale, eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees) were identified as the optimal models for rice AGB estimation due to their stable performance across all stages, with R2 values consistently above 0.74. Finally, rice growth classification maps and corresponding fertilization recommendations were generated based on the satellite-scale inversion results, providing technical support for precision agriculture practices. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121930 |
