# overview
Tianyi Wang is a postdoctoral research associate at Texas A&M University. He received his BS degree in agricultural engineering from China Agricultural University in 2012, his MS degree in agricultural engineering from Pennsylvania State University in 2014, and his Ph.D. in agricultural engineering from Texas A&M University in 2020.

Tianyi Wang is serving as associate editor of Transactions of the ASABE and Applied Engineering in Agriculture. His current research interests include UAV-based remote sensing, machine learning, image analysis, cotton engineering, turf grass breeding, and smart agricultural equipment development.

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4603 Computer Vision And Multimedia Computation

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4603 Computer Vision And Multimedia Computation

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Machine Learning And Artificial Intelligence

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China Agricultural University - (Beijing, China)

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Texas A&M University - (College Station, Texas, United States)

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Pennsylvania State University - (State College, Pennsylvania, United States)

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Texas A&M University - (College Station, Texas, United States)

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Automatic Classification of Cotton Root Rot Disease Based on UAV Remote Sensing

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A transformer-based image detection method for grassland situation of alpine meadows

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Biofilm response in potential difference-enhanced membrane-aerated biofilm systems for accelerated antibiotic removal and ARG mitigation.

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Innovative Lactic Acid Production Techniques Driving Advances in Silage Fermentation

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A smart cotton module tracking and monitoring system for handling logistics and cover damage

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RepGhostConv-Subpixel Fusion-Cascading Attention-Detection (RSCDet): A novel lightweight model for real-time aphid detection

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GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

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High-throughput phenotyping techniques for forage: Status, bottleneck, and challenges

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A novel estimation method of grassland Fractional vegetation cover based on multi-sensor data fusion

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Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages

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A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based on UAV Remote Sensing

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Unmanned aerial vehicle remote sensing to delineate cotton root rot

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SPIE, the international society for optics and photonics

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Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping.

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Plant-by-Plant Level Classification of Cotton Root Rot by UAV Remote Sensing

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SPIE, the international society for optics and photonics

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Wheat Lodging Segmentation Based on Lstm_PSPNet Deep Learning Network

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Optimization design method for typical grassland perception robot system

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Biomechanical properties and discrete element modeling of PSR stalks during silage harvest

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Associations between prediagnostic aspirin use and ovarian tumor gene expression.

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Effects of plasma pre-treatment on field germination and seedling growth of Platycodon grandiflorum

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Lifetime ovulatory years and ovarian cancer gene expression profiles.

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Disease Detection and Mitigation in a Cotton Crop with UAV Remote Sensing

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SPIE, the international society for optics and photonics

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Energy Consumption of Herbaceous Biomass Bulk Densification

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An Adaptive Toolkit for Projecting the Impact of Green Infrastructure Provisions on Stormwater Runoff and Pollutant Load-A Case Study on the City of Galena Park, Texas, USA.

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Field-region and plant-level classification of cotton root rot based on UAV remote sensing

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American Society of Agricultural and Biological Engineers (ASABE)

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Autonomous mobile ground control point for accurate UAV remote sensing in agriculture (Conference Presentation)

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SPIE, the international society for optics and photonics

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Volunteer Cotton Habitat Prediction Model and Detection with UAV Remote Sensing

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American Society of Agricultural and Biological Engineers (ASABE)

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Round Modules: Update on Handling Logistics and Cover Damage

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American Society of Agricultural and Biological Engineers (ASABE)

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UAV remote sensing based estimation of green cover during turfgrass establishment

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Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References.

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Autonomous Mobile Ground Control Point Improves Accuracy of Agricultural Remote Sensing through Collaboration with UAV

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A method for estimating the height of Achnatherum splendens based on image processing

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YOLO-Sp: A Novel Transformer-Based Deep Learning Model for Achnatherum splendens Detection

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Topics in Measurement Error Analysis and High-Dimensional Binary Classification