machine learning; deep learning; computer vision and its applications in agriculture and remote sensing.

Recent papers：

[1] Li K, Qi M, Zhuang S, et al. Noise-aware infrared polarization image fusion based on salient prior with attention-guided filtering network[J]. Optics Express, 2023, 31(16): 25781-25796.

[2] Qi M, Liu L, Zhuang S*, et al. FTC-net: fusion of transformer and CNN features for infrared small target detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8613-8623.

[3] Li K, Qi M, Zhuang S, et al. TIPFNet: a transformer-based infrared polarization image fusion network[J]. Optics Letters, 2022, 47(16): 4255-4258.

[4] Zhuang S, Wang P, Wang G, et al. Improving remote sensing image captioning by combining grid features and transformer[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.

[5] Wang D, Wang P, Zhuang S, et al. Asymptotic analysis of locally weighted jackknife prediction[J]. Neurocomputing, 2020, 417: 10-22.

[6] Zhuang S, Wang P, Jiang B. Vegetation extraction in the field using multi-level features[J]. Biosystems Engineering, 2020, 197: 352-366.

[7] Zhuang S, Wang P, Jiang B, et al. Learned features of leaf phenotype to monitor maize water status in the fields[J]. Computers and electronics in agriculture, 2020, 172: 105347.

[8] Zhuang S, Wang P, Jiang B, et al. A single shot framework with multi-scale feature fusion for geospatial object detection[J]. Remote Sensing, 2019, 11(5): 594.

[9] Zhuang S, Wang P, Jiang B. Segmentation of green vegetation in the field using deep neural networks[C]//2018 13th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2018: 509-514.

[10] Zhuang S, Wang P, Jiang B, et al. Early detection of water stress in maize based on digital images[J]. Computers and Electronics in Agriculture, 2017, 140: 461-468.

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