FC_2025v8n4

Field Crop 2025, Vol.8, No.4, 204-212 http://cropscipublisher.com/index.php/fc 210 Cen H., Wan L., Zhu J., Li Y., Li X., Zhu Y., Weng H., Wu W., Yin W., Xu C., Bao Y., Feng L., Shou J., and He Y., 2019, Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras, Plant Methods, 15(1): 32. https://doi.org/10.1186/s13007-019-0418-8 Chen B., Su Q., Li Y., Chen R., Yang W., and Huang C., 2025, Field rice growth monitoring and fertilization management based on UAV spectral and deep image feature fusion, Agronomy, 15(4): 886. https://doi.org/10.3390/agronomy15040886 Chen C., Bao Y., Zhu F., and Yang R., 2024, Remote sensing monitoring of rice growth under Cnaphalocrocis medinalis (Guenée) damage by integrating satellite and UAV remote sensing data, International Journal of Remote Sensing, 45(3): 772-790. https://doi.org/10.1080/01431161.2024.2302350 Dimyati M., Supriatna S., Nagasawa R., Pamungkas F., and Pramayuda R., 2023, A comparison of several UAV-based multispectral imageries in monitoring rice paddy (a case study in paddy fields in Tottori prefecture, Japan), ISPRS International Journal of Geo-Information, 12(2): 36. https://doi.org/10.3390/ijgi12020036 Du X., Zheng L., Zhu J., and He Y., 2024, Enhanced leaf area index estimation in rice by integrating UAV-based multi-source data, Remote Sensing, 16(7): 1138. https://doi.org/10.3390/rs16071138 Duan B., Liu Y., Gong Y., Peng Y., Wu X., Zhu R., and Fang S., 2019, Remote estimation of rice LAI based on Fourier spectrum texture from UAV image, Plant Methods, 15(1): 124. https://doi.org/10.1186/s13007-019-0507-8 Fatchurrachman Soh N., Shah R., Giap S., Setiawan B., and Minasny B., 2023, Automated near-real-time mapping and monitoring of rice growth extent and stages in Selangor Malaysia, Remote Sensing Applications: Society and Environment, 31: 100993. https://doi.org/10.1016/j.rsase.2023.100993 Gade S., Madolli M., García-Caparrós P., Ullah H., Cha-Um S., Datta A., and Himanshu S., 2024, Advancements in UAV remote sensing for agricultural yield estimation: a systematic comprehensive review of platforms, sensors, and data analytics, Remote Sensing Applications: Society and Environment, 37: 101418. https://doi.org/10.1016/j.rsase.2024.101418 Ge J., Zhang H., Xu L., Huang W., Jiang J., Song M., Guo Z., and Wang C., 2024, Full cycle rice growth monitoring with dual-pol SAR data and interpretable deep learning, International Journal of Digital Earth, 17(1): 2445639. https://doi.org/10.1080/17538947.2024.2445639 Gong Y., Yang K., Lin Z., Fang S., Wu X., Zhu R., and Peng Y., 2021, Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season, Plant Methods, 17(1): 88. https://doi.org/10.1186/s13007-021-00789-4 Guo X., Ou Y., Deng K., Fan X., Gao R., and Zhou Z., 2025, A unmanned aerial vehicle-based image information acquisition technique for the middle and lower sections of rice plants and a predictive algorithm model for pest and disease detection, Agriculture, 15(7): 790. https://doi.org/10.3390/agriculture15070790 Guo X., Yin J., Li K., Yang J., Zou H., and Yang F., 2023, Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods, Frontiers of Earth Science, 18(1): 30-43. https://doi.org/10.1007/s11707-022-1011-4 Guo Y., Fu Y., Hao F., Zhang X., Wu W., Jin X., Bryant C., and Senthilnath J., 2021, Integrated phenology and climate in rice yields prediction using machine learning methods, Ecological Indicators, 120: 106935. https://doi.org/10.1016/J.ECOLIND.2020.106935 Iatrou M., Karydas C., Iatrou G., Pitsiorlas I., Aschonitis V., Raptis I., Mpetas S., Kravvas K., and Mourelatos S., 2021, Topdressing nitrogen demand prediction in rice crop using machine learning systems, Agriculture, 11(4): 312. https://doi.org/10.3390/AGRICULTURE11040312 Islam M., Di L., Qamer F., Shrestha S., Guo L., Lin L., Mayer T., and Phalke A., 2023, Rapid rice yield estimation using integrated remote sensing and meteorological data and machine learning, Remote Sensing, 15(9): 2374. https://doi.org/10.3390/rs15092374 Jiang Z., Tu H., Bai B., Yang C., Zhao B., Guo Z., Liu Q., Zhao H., Yang W., Xiong L., and Zhang J., 2021, Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress, New Phytologist, 232(1): 440-455. https://doi.org/10.1111/nph.17580 Jin Z., Guo S., Li S., Yu F., and Xu T., 2024, Research on the rice fertiliser decision-making method based on UAV remote sensing data assimilation, Computers and Electronics in Agriculture, 216: 108508. https://doi.org/10.1016/j.compag.2023.108508 Kharim M., Wayayok A., Abdullah A., Shariff A., Husin E., and Mahadi M., 2022, Predictive zoning of pest and disease infestations in rice field based on UAV aerial imagery, The Egyptian Journal of Remote Sensing and Space Science, 25(3): 831-840. https://doi.org/10.1016/j.ejrs.2022.08.001 Ko J., Shin T., Kang J., Baek J., and Sang W., 2024, Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation, Frontiers in Plant Science, 15: 1320969. https://doi.org/10.3389/fpls.2024.1320969

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