Field Crop 2025, Vol.8, No.4, 204-212 http://cropscipublisher.com/index.php/fc 211 Kumar M., Bhattacharya B., Pandya M., and Handique B., 2024, Machine learning based plot level rice lodging assessment using multi-spectral UAV remote sensing, Computers and Electronics in Agriculture, 219: 108754. https://doi.org/10.1016/j.compag.2024.108754 Li J.Q., and Jiong F., 2024, Genomic diversity and evolutionary mechanisms in the Oryza genus: a comparative analysis, Genomics and Applied Biology, 15(1): 54-63. https://doi.org/10.5376/gab.2024.15.0008 Li J., Lu J., Fu H., Zou W., Zhang W., Yu W., and Feng Y., 2024, Research on the inversion of key growth parameters of rice based on multisource remote sensing data and deep learning, Agriculture, 14(12): 2326. https://doi.org/10.3390/agriculture14122326 Liao M., Wang Y., Chu N., Li S., Zhang Y., and Lin D., 2025, Mature rice biomass estimation using UAV-derived RGB vegetation indices and growth parameters, Sensors, 25(9): 2798. https://doi.org/10.3390/s25092798 Liu S., Zhang B., Yang W., Chen T., Zhang H., Lin Y., Tan J., Li X., Gao Y., Yao S., Lan Y., and Zhang L., 2023, Quantification of physiological parameters of rice varieties based on multi-spectral remote sensing and machine learning models, Remote Sensing, 15(2): 453. https://doi.org/10.3390/rs15020453 Luo S., Jiang X., Jiao W., Yang K., Li Y., and Fang S., 2022, Remotely sensed prediction of rice yield at different growth durations using UAV multispectral imagery, Agriculture, 12(9): 1447. https://doi.org/10.3390/agriculture12091447 Luu T., Tam N., Phuc P., Nguyen H., Van Le L., and Ngo Q., 2023, Evaluation of land roughness and weather effects on paddy field using cameras mounted on drone: a comprehensive analysis from early to mid-growth stages, Journal of King Saud University-Computer and Information Sciences, 35(10): 101853. https://doi.org/10.1016/j.jksuci.2023.101853 Lyu J., 2024, High yield strategies in rice cultivation: agronomic practices and innovations, Bioscience Evidence, 14(6): 270-280. https://doi.org/10.5376/be.2024.14.0028 Lyu M., Lu X., Shen Y., Tan Y., Wan L., Shu Q., He Y., He Y., and Cen H., 2023, UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding, Agricultural and Forest Meteorology, 341: 109646. https://doi.org/10.1016/j.agrformet.2023.109646 Ma B., Cao G., Hu C., and Chen C., 2023, Monitoring the rice panicle blast control period based on UAV multispectral remote sensing and machine learning, Land, 12(2): 469. https://doi.org/10.3390/land12020469 Prabhakar M., Gopinath K., Kumar N., Thirupathi M., Sravan U., Kumar G., Siva G., Chandana P., and Singh V., 2024, Mapping leaf area index at various rice growth stages in Southern India using airborne hyperspectral remote sensing, Remote Sensing, 16(6): 954. https://doi.org/10.3390/rs16060954 Qiu Z., Ma F., Li Z., Xu X., Ge H., and Du C., 2021, Estimation of nitrogen nutrition index in rice from UAV RGB images coupled with machine learning algorithms, Computers and Electronics in Agriculture, 189: 106421. https://doi.org/10.1016/j.compag.2021.106421 Qiu Z., Xiang H., Ma F., and Du C., 2020, Qualifications of rice growth indicators optimized at different growth stages using unmanned aerial vehicle digital imagery, Remote Sensing, 12(19): 3228. https://doi.org/10.3390/rs12193228 Ramadhani F., Pullanagari R., Kereszturi G., and Procter J., 2020, Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning, International Journal of Remote Sensing, 41(21): 8428-8452. https://doi.org/10.1080/01431161.2020.1779378 Sari M., Hassim Y., Hidayat R., and Ahmad A., 2021, Monitoring rice crop and paddy field condition using UAV RGB imagery, JOIV: International Journal on Informatics Visualization, 5(4): 469-474. https://doi.org/10.30630/joiv.5.4.742 Sarkar T., Roy D., Kang Y., Jun S., Park J., and Ryu C., 2023, Ensemble of machine learning algorithms for rice grain yield prediction using UAV-based remote sensing, Journal of Biosystems Engineering, 49(1): 1-19. https://doi.org/10.1007/s42853-023-00209-6 Shen Y., Yan Z., Yang Y., Tang W., Sun J., and Zhang Y., 2024, Application of UAV-Borne visible-infared pushbroom imaging hyperspectral for rice yield estimation using feature selection regression methods, Sustainability, 16(2): 632. https://doi.org/10.3390/su16020632 Sheng R., Huang Y., Chan P., Bhat S., Wu Y., and Huang N., 2022, Rice growth stage classification via RF-based machine learning and image processing, Agriculture, 12(12): 2137. https://doi.org/10.3390/agriculture12122137 Singha C., and Swain K., 2023, Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud, Remote Sensing Applications: Society and Environment, 32: 101029. https://doi.org/10.1016/j.rsase.2023.101029 Tan S., Liu J., Lu H., Lan M., Yu J., Liao G., Wang Y., Li Z., Qi L., and Ma X., 2022, Machine learning approaches for rice seedling growth stages detection, Frontiers in Plant Science, 13: 914771. https://doi.org/10.3389/fpls.2022.914771
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