余润 讲师（高校）林学院

未发表论文：

In Peer Review or Under Revision

1. You, Z., Yu, R.^*, Hong, H., Wu, S., Zhang, F.*, 2026. Texture indices play an important role in pre-visual early-stage detection of pine wilt disease: A case study in Pinus massoniana. In Review

2. Yu, R., et al., 2026. Remote sensing for early detection of pine wilt disease (Bursaphelenchus xylophilus): A critical review. In Revision

已发表论文：

Peer-reviewed publications

<2026>

1. Guo, Q., Li, L.*, Yu, R., Zhang, Z., Huang, H., 2026. Simulation-to-Reality transfer learning with UAV-based imagery for forest inventory attribute prediction. Comput. Electron. Agric. 241:111262.

2. You, Z.¹, Yu, R.¹, Hong, H., Wu, S.*, Zhang, F.*, 2026. Retrieval of pigment content and water status of Pinus massoniana under pine wood nematode stress using multitemporal UAV multispectral imagery. Pest Manag. Sci. https://doi.org/10.1002/ps.70957（学生共一第一）

3. Yu, R, Li, X., Luo, J., Qiu, Y., Lu, X., Wu, S., Zhang, F., Huang, S.*, 2026. OpenPineDet: An open-set detection framework for the detection of dead pine wood in forests. Int. J. Appl. Earth Obs. Geoinf. 151:105405.

4. Li, X., Yu, R*, Qiu, Y., Luo, J., Lu, X., Huang, S., Wu, S., Zhang, F., 2026. Dead pine tree detection using an iterative semi-supervised framework integrating expert annotation and active learning. Comput. Electron. Agric. 251:112005

5. 俞琳锋, 王峰, 余润,吴昊, 宗世祥, 骆有庆, 任利利. 同林分松科4属树种对松材线虫病的抗性评价及长时序动态监测[J]. 北京林业大学学报, 2026, 48 (01): 41-49.

<2025>

1.   Yu, R., Liu, Y., Gao, B., Ren, L., Luo, Y.*, 2025. Detection of pine wood nematode infections in Chinese pine (Pinus tabuliformis) using hyperspectral drone images. Pest Manag. Sci. 81(6).

2.       Liu, Y.*, Luo, Y., Yu, R., Ren, L., Jiang, Q., He, S., Chen, X., Yang, G., 2025. Combined use of spectral and structural features for improved early detection of pine shoot beetle attacks in Yunnan pines. Remote Sens. 17(7):1109.

<2024>

1.       Yu, R., Luo, Y.*, Ren, L., 2024. Detection of pine wood nematode infestation using hyperspectral drone images. Ecol. Indic. 162:112034.

2.       Huo, L.*, Yu, R., Eva, L., Henrik, J., Jonas, B., Li, N., 2024. Influence of crown pixel selection on the early detection of bark veetle infestations using multispectral drone images. IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 5218–5221.

<2023>

1.    Wu, D., Yu, L., Yu, R., Zhou, Q., Li, J., Zhang, X., Ren, L., Luo, Y.*, 2023. Detection of the monitoring window for pine wilt disease using multi-temporal UAV-based multispectral imagery and machine learning algorithms. Remote Sens. 15(2):444.

<2022>

1.     Yu, R., Huo, L., Huang, H., Yuan, Y., Gao, B., Liu, Y., Yu, L., Li, H., Yang, L., Ren, L., Luo, Y.*, 2022. Early detection of pine wilt disease tree candidates using time-series of spectral signatures. Front. Plant Sci. 13:1000093.

2.       袁源, 任利利, 余润, 李佳星, 石丰铭, 曾建华, 骆有庆. 辽宁抚顺松材线虫入侵松林内天牛种类调查[J]. 应用昆虫学报. 2022, 59(02):446–456.

<2021>

1.       Yu, R., Luo, Y.*, Zhou, Q., Zhang, X., Wu, D., Ren, L., 2021. A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level. Int. J. Appl. Earth Obs. Geoinf. 101:102363.

2.     Yu, R., Luo, Y.*, Zhou, Q., Zhang, X., Wu, D., Ren, L., 2021. Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery. Forest Ecol. Manag. 497:119493.（ESI高被引论文，他引231次）

3.       Yu, R., Ren, L., Luo, Y.*, 2021. Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery. For. Ecosyst. 8(44), 583–601.

4.       Yu, R., Luo, Y.*, Li, H., Yang, L., Huang, H., Yu, L., Ren, L., 2021. Three-dimensional convolutional neural network model for early detection of pine wilt disease using UAV-based hyperspectral images. Remote Sens. 13(20):4065.