Wenxuan Guo

Associate Professor

Texas A&M AgriLife Research

United States

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Wenxuan Guo is an Associate Professor at Texas A&M AgriLife Research in the United States. His research areas focus on the application of unmanned aerial systems (UAS) in agriculture, particularly in crop yield simulation, water stress assessment, and plant health monitoring. He has contributed to advancements in resource-efficient UAS technologies, including deep learning for image analysis and calibration techniques. His recent publications address critical issues in cotton cultivation and precision vineyard management.

Articles (16)

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2022

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2022

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2022

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2021

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2021

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2020

Collaborators (3)

Glen Ritchie

Associate Professor

Texas A&M AgriLife

UNITED STATES

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Lindsey Slaughter

Associate Professor

Texas Tech University

UNITED STATES

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Sukhbir Singh

Assistant Professor

Texas Tech University

UNITED STATES

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Wenxuan Guo

Research Interests

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Articles (16)

Toward resource-efficient UAV systems: Deep learning model compression for onboard-ready weed detection in UAV imagery

Open cotton boll detection using LiDAR point clouds and RGB images from unmanned aerial systems

Simulating crop yields and water productivity for three cotton-based cropping systems in the Texas High Plains

Water Stress Assessment of Cotton Cultivars Using Unmanned Aerial System Images

Flight Altitude and Sensor Angle Affect Unmanned Aerial System Cotton Plant Height Assessments

Dual-stage color calibration of UAV imagery using multivariate regression and deep learning

Spatial and temporal patterns of cotton profitability in management zones based on soil properties and topography

Assessing grapevine vigor as affected by soil physicochemical properties and topographic attributes for precision vineyard management

Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model

Simulation of root zone soil water dynamics under cotton-silverleaf nightshade interactions in drip-irrigated cotton

Coupling effects of reduced nitrogen, phosphorus and potassium on drip-irrigated cotton growth and yield formation in Northern Xinjiang

Spatial patterns of soil microbial communities and implications for precision soil management at the field scale

Correction: Lin, Z.; Guo, W. Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models. Remote Sens. 2021, 13, 2822

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery

Collaborators (3)

Glen Ritchie

Lindsey Slaughter

Sukhbir Singh

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