鍙戣〃璁烘枃 绗竴浣滆€呮垨閫氳浣滆€呭彂琛ㄨ鏂囷細 [1] An explainable XGBoost model improved by SMOTE-ENN technique for maize lodging detection based on multi-source unmanned aerial vehicle images [J]. Computers and Electronics in Agriculture, 2022, 194:106804. 锛堜簩鍖猴紝TOP锛堻/span> [2] Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data [J]. Plant Methods, 2019, 15(1): 10.锛堜簩鍖猴紝SCI锛堻/span> [3] Combining Self-Organizing Maps and Biplot Analysis to Preselect Maize Phenotypic Components based on UAV High-Throughput phenotyping platform [J]. Plant Methods, 2019,15(1):57.锛堜簩鍖猴紝SCI锛堻/span> [4] Clustering Field-Based Maize Phenotyping of Plant-Height Growth and Canopy Spectral Dynamics Using a UAV Remote-Sensing Approach [J]. Frontiers in Plant Science, 2018, 9: 1638.锛堜簩鍖猴紝SCI锛堻/span> [5] Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation [J]. Remote Sensing, 2018, 10(10): 1528.锛堜簩鍖猴紝SCI锛堻/span> [6] Fuzzy Clustering of Maize Plant-Height Patterns Using Time Series of UAV Remote-Sensing Images and Variety Traits[J]. Frontiers in Plant Science, 2019, 10(926).锛堜簩鍖猴紝SCI锛堻/span> [7]鑳芥簮鍨嬪煄甯傛櫙瑙傛牸灞€鏃剁┖婕斿彉涓庨┍鍔ㄦ満鍒剁殑閲忓寲鐮旂┒鈥斺€斾互灞辫タ鐪佸ぇ鍚屽競涓轰緥[J].灞辫タ澶у悓澶у瀛︽姤(鑷劧绉戝鐗?,2024,40(06):118-123.锛堥€氳浣滆€咃級 瀛︽湳涓撹憲锛氥€婁綔鐗╄〃鍨嬮仴鎰熶笌棰勬祴寤烘ā銆嬶紝姝︽眽澶у鍑虹増绀撅紝2025 |