WEI GUO

Associate Professor

University of Tokyo

Japan

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About

Wei Guo is an Associate Professor at The University of Tokyo, Japan. His research areas encompass 3D phenotyping, agricultural automation, and quantitative genetics as demonstrated in his recent articles. Notable works include advancements in point cloud completion networks for grape phenotyping and improvements in crop segmentation techniques in natural agricultural settings. His work also focuses on innovative methods for tree biomass measurement and fruit detection using UAV technology.

Articles (19)

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2025

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2022

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2022

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2022

Collaborators (7)

Kazutaka Kusunoki

JAPAN

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Sachiko Isobe

Professpr

Graduate School of Agricultural and Life Sciences, The University of Tokyo

JAPAN

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Pieter Blok

Assistant Professor

University of Tokyo

JAPAN

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Boris Rewald

AUSTRIA

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Hiroyoshi Iwata

Professor

University of Tokyo

JAPAN

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Irena Drofova

Tomas Bata University in Zlín

CZECH REPUBLIC

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Seishi Ninomiya

Prof.

University of Tokyo

JAPAN

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WEI GUO

Research Interests

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Articles (19)

GrapeCPNet: A self-supervised point cloud completion network for 3D phenotyping of grape bunches

A Patch-Level Data Synthesis Pipeline Enhances Species-Level Crop and Weed Segmentation in Natural Agricultural Scenes

Water involved in transformation of soil organo-mineral fractions during catalytic thermal desorption of dioxins-like polychlorinated biphenyls

Improved Panicle Counting With Density-Aware Sampling and Enhanced Matching

A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees

EasyDAM_V4: Guided-GAN-based cross-species data labeling for fruit detection with significant shape difference

DomAda-FruitDet: Domain-Adaptive Anchor-Free Fruit Detection Model for Auto Labeling

Quantitative Genetic Aspects of Accuracy of Tree Biomass Measurement Using LiDAR

Multi-Scale Attention Network for Vertical Seed Distribution in Soybean Breeding Fields

Automatic Branch–Leaf Segmentation and Leaf Phenotypic Parameter Estimation of Pear Trees Based on Three-Dimensional Point Clouds

Use of scanning devices for object 3D reconstruction by photogrammetry and visualization in virtual reality

Tree Branch Skeleton Extraction from Drone-Based Photogrammetric Point Cloud

Drone-Based Harvest Data Prediction Can Reduce On-Farm Food Loss and Improve Farmer Income

EasyDAM_V3: Automatic Fruit Labeling Based on Optimal Source Domain Selection and Data Synthesis via a Knowledge Graph

Image-Based Phenotyping for Non-Destructive In Situ Rice (Oryza sativa L.) Tiller Counting Using Proximal Sensing

Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

How Useful Is Image-Based Active Learning for Plant Organ Segmentation?

Augmentation Method for High Intra-Class Variation Data in Apple Detection

EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection

Collaborators (7)

Kazutaka Kusunoki

Sachiko Isobe

Pieter Blok

Boris Rewald

Hiroyoshi Iwata

Irena Drofova

Seishi Ninomiya

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