Remote Sensing Technology and Application, Volume. 40, Issue 1, 69(2025)
Remote Sensing Monitoring of Wheat Stripe Rust Using Constrained Random Forest and Bayesian Optimization Algorithm
[1] CHEN Wanquan, KANG Zhensheng, MA Zhanhong et al. Integrated management of wheat stripe rust caused by puccinia striiformis f.sp. tritici in China. Scientia Agricultura Sinica, 46, 4254-4262(2013).
[2] NDUKU L, MUNGHEMEZULU C, MASHABA-MUNGHEMEZULU Z et al. Global research trends for unmanned aerial vehicle remote sensing application in wheat crop monitoring. Geomatics, 3, 115-136(2023).
[3] JING Xia, ZOU Qin, BAI Zongfan et al. Research progress of crop diseases monitoring based on reflectance and chlorophyll fluorescence data. Acta Agronomica Sinica, 47, 2067-2079(2021).
[4] LIU Qi, GU Yilin, WANG Cuicui et al. Canopy hyperspectral features analysis of latent period wheat stripe rust based on discriminant partial least squares. Journal of Plant Protection, 45, 138-145(2018).
[5] YUE Xuejun, SONG Qingkui, LI Zhiqing et al. Research status and prospect of crop information monitoring technology in field. Journal of South China Agricultural University, 44, 43-56(2023).
[6] ZHANG Liangliang, ZHANG Zhao, CAO Juan et al. Rapid assessment of maize chilling damage based on GEE. Journal of remote sensing, 24, 1206-1220(2020).
[7] XIE L, ZHANG R, ZHAN J. Wildfire risk assessment in Liangshan Prefecture, China based on an integration machine learning algorithm. Remote Sensing, 14, 4592(2022).
[8] JING Xia, Xiaoyan LÜ, ZHANG Chao et al. Early detection of winter Wheat Stripe rust based on Slf-PLS Model. Transactions of the Chinese Society of Agricultural Machinery, 51, 191-197(2020).
[9] CONRAD A O, LI W, LEE D Y et al. Machine learning-based presymptomatic detection of rice sheath blight using spectral profiles. Plant Phenomics, 8954085(2020).
[10] JIN Hang, JING Xia, GAO Yuan et al. GBRT model for dete-cting the severity of wheat stripe rust by remote sensing. Remote Sensing Technology and Application, 36, 411-419(2021).
[11] JING X, ZOU Q, YAN J et al. Remote sensing monitoring of winter wheat stripe rust based on mRMR-XGBoost algorithm. Remote Sensing, 14, 756(2022).
[12] BREIMAN L. Random forests. Machine Learning, 45, 5-32(2001).
[13] ZHANG W, HE Y, WANG L et al. Landslide susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing. Geological Journal, 1-16(2023).
[14] LI Jianli, DONG Yingying, SHI Yue et al. Remote sensing monitoring of wheat powdery mildew based on random forest model. Journal of Plant Protection, 45, 395-396(2018).
[15] BAI Zongfan, JING Xia, ZHANG Teng et al. Canopy SIF synergize with total spectral reflectance optimized by the MDBPSP algorithm to monitor wheat stripe rust. Acta Agronomica Sinica, 46, 1248-1257(2020).
[16] DUAN Weina, JING Xia, LIU Liangyun et al. Monitoring of wheat Stripe rust based on integration of SIF and Reflectance Spectrum. Spectroscopy and Spectral Analysis, 42, 859-865(2022).
[17] LV Hongyan, FENG Qian. A review of random forests algorithm. Journal of the Hebei Academy of Sciences, 36, 37-41(2019).
[18] COULSTON J W, BLINN C E, THOMAS V A et al. Appro-ximating prediction uncertainty for random forest regression models. Photogrammetric Engineering & Remote Sensing, 82, 189-197(2016).
[19] LI Jiangeng, GAO Zhikun. Setting of class weights in random forest for small-sample data. Computer Engineering and Applications, 45, 131-134(2009).
[20] WU Linsheng, Zhang Yongguang, Zhang Zhaoying et al. Remote sensing of solar-induced chlorophyll fluorescence and its applications in terrestrial ecosystem monitoring. Chinese Journal of Plant Ecology, 46, 1167-1199(2022).
[21] YAN Yuxing, Xiaoliang LÜ, WANG Yakai et al. Bibliometric analysis of research and application of Solar-Induced Chlorophyll Fluorescence. Chinese Journal of Agrometeorology, 44, 106-122(2023).
[22] XU S, LIU Z, HAN S et al. Exploring the sensitivity of solar-induced chlorophyll fluorescence at different wavelengths in response to drought. Remote Sensing, 15, 1077(2023).
[23] YANG Ni, DENG Shulin, FAN Yanhong et al. Research progress on chlorophyll fluorescence remote sensing inversion and its application in agricultural monitoring. Jiangsu Agricultural Sciences, 51, 1-13(2023).
[24] HU Jiaochan, LIU Liangyun, LIU Xinjie. Assessing uncertainties of sun-induced chlorophyll fluorescence retrieval using FluorMOD model. Journal of Remote Sensing, 19, 594-608(2015).
[25] GAO Honglei, Changqian MEN, WANG Wenjian. Algorithm for model decision tree with multi-kernel Bayesian optimization. Journal of National University of Defense Technology, 44, 67-76(2022).
[26] DONG Bo, CHEN Airui, ZHANG Ming. The role of machine learning in solving overfitting. Journal of Psychological Science, 44, 274-281(2021).
[27] ZHANG Jie, XU Bo, FENG Haikuan et al. Monitoring nitrogen nutrition and grain protein content of rice based on ensemble learning. Spectroscopy and Spectral Analysis, 42, 1956-1964(2022).
[28] LIU J, JIANG L, CHEN Y et al. Study on prediction model of liquid hold up based on random forest algorithm. Chemical Engineering Science, 268, 118383(2022).
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Yiyang XUE, Xia JING, Qixing YE, Kaiqi Du, Bingyu Li. Remote Sensing Monitoring of Wheat Stripe Rust Using Constrained Random Forest and Bayesian Optimization Algorithm[J]. Remote Sensing Technology and Application, 2025, 40(1): 69
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Received: May. 30, 2023
Accepted: --
Published Online: May. 22, 2025
The Author Email: Xia JING (jingxiaxust@163.com)