NOVEL APPROACH FOR ESTIMATING NITROGEN CONTENT IN PADDY FIELDS USING LOW ALTITUDE REMOTE SENSING SYSTEM

8 citations as recorded by crossref.

1. Improving sugar beet canopy mapping through UAV image analysis J. Jiang et al. https://doi.org/10.1038/s41598-025-23868-1
2. Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms Z. Qiu et al. https://doi.org/10.1016/j.compag.2021.106421
3. Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery D. Stavrakoudis et al. https://doi.org/10.3390/rs11050545
4. Development of a color-based, non-destructive method to determine leaf N levels of Hass avocado under field conditions Á. Gallegos et al. https://doi.org/10.1016/j.atech.2025.100895
5. Estimation of chlorophyll content in cotton leaves using UAV RGB imagery Y. Huang et al. https://doi.org/10.3389/fpls.2026.1794351
6. Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping X. Li et al. https://doi.org/10.3390/s23073756
7. An unmanned aerial vehicle-based cotton nitrogen nutrition index estimation model utilizing feature selection and machine learning C. Xiao et al. https://doi.org/10.1016/j.compag.2025.110798
8. Multi-Angle Detection of Spatial Differences in Tea Physiological Parameters D. Duan et al. https://doi.org/10.3390/rs15040935