A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery. - Biyao Zhang; Huichun Ye; Wei Lu; Wenjiang Huang; Bo Wu; Zhuoqing Hao; Hong Sun (Remote. Sens., 2021)

Spatiotemporal Distribution and Main Influencing Factors of Grasshopper Potential Habitats in Two Steppe Types of Inner Mongolia, China. - Jing Guo; Longhui Lu; Yingying Dong; Wenjiang Huang; Bing Zhang 001; Bobo Du; Chao Ding; Huichun Ye; Kun Wang 033; Yanru Huang; Zhuoqing Hao; Mingxian Zhao; et al. (Remote. Sens., 2023)

Spatiotemporal monitoring of grasshopper habitats using multi-source data: Combined with landscape and spatial heterogeneity. - Jing Guo; Wenjiang Huang; Yingying Dong; Kejian Lin; Yantao Zhou; Ning Wang; Rui Hua; Zhuoqing Hao; Xiaolong Ding; Fei Zhao (Int. J. Appl. Earth Obs. Geoinformation, 2024)

Detection of Rubber Tree Powdery Mildew from Leaf Level Hyperspectral Data Using Continuous Wavelet Transform and Machine Learning. - Xiangzhe Cheng; Yuyun Feng; Anting Guo; Wenjiang Huang; Zhiying Cai; Yingying Dong; Jing Guo; Binxiang Qian; Zhuoqing Hao; Guiliang Chen; Yixian Liu (Remote. Sens., 2024)

Early Detection of Rubber Tree Powdery Mildew by Combining Spectral and Physicochemical Parameter Features. - Xiangzhe Cheng; Mengning Huang; Anting Guo; Wenjiang Huang; Zhiying Cai; Yingying Dong; Jing Guo; Zhuoqing Hao; Yanru Huang; Kehui Ren; Bohai Hu; Guiliang Chen; et al. (Remote. Sens., 2024)

Predicting the Global Potential Suitable Distribution of Fall Armyworm and Its Host Plants Based on Machine Learning Models. - Yanru Huang; Yingying Dong; Wenjiang Huang; Jing Guo; Zhuoqing Hao; Mingxian Zhao; Bohai Hu; Xiangzhe Cheng; Minghao Wang (Remote. Sens., 2024)

Improved pest and disease spread model for simulating the spread of invasive species: A case study of pine wilt disease. - Zhuoqing Hao; Wenjiang Huang; Biyao Zhang; Yifan Chen; Guofei Fang; Jing Guo; Yanru Huang; Xiangzhe Cheng; Bohai Hu (Comput. Electron. Agric., 2025)