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Wen-Hao Su

Post： Senior Research Fellow

Duties：

Research Label： Agricultural Intelligent Robot

Degree： Ph.D

Tel：

Email： wenhao.su@cau.edu.cn

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About

  • Department: Agricultural Engineering
  • Gender: male
  • Nationality:
  • Post: Senior Research Fellow
  • Duties:
  • Research Label: Agricultural Intelligent Robot
  • Graduate School: University College Dublin (UCD)
  • Degree: Ph.D
  • Tel:
  • Email: wenhao.su@cau.edu.cn
  • Office Location: 232, College of Engineering
  • Address: 17 Qinghua East Road, Haidian, Beijing
  • PostCode: 100083
  • Fax: +86-10-62736428

Expert category

  • Academic degree supervisor type:
  • Professional Degree Graduate Supervisor Type:
  • Engaged in disciplines1:
  • Engaged in disciplines2:
  • Engaging in a profession1:
  • Engaging in a profession2:
  • Research direction 2:
  • Research direction 1:
  • Name of professional degree field:

Resume

Prof. Wen-Hao Su is a distinguished research fellow in Agricultural Intelligent Equipment Engineering at China Agricultural University. He focuses on the development of smart systems and robots for crop growth and food quality and safety to meet the planet's need for plentiful, nutritious and flavorful food supplies. 


From 2016 to 2020, he successively worked at the University of Birmingham (UK), University of California, Davis (UCD), United States Department of Agriculture-Agricultural Research Service (USDA-ARS) and University of Minnesota. Moreover, he was a visiting scholar at University of Copenhagen (Denmark) and University of Santiago de Compostela (Spain) from 2015 to 2016. Prof. Su got his PhD degree in Biosystems and Food Engineering, from University College Dublin (UCD), National University of Ireland.

Teaching research

Prof. Su is advancing the leading edge of engineering knowledge to agriculture, having developed reliable robotic sensing systems for achieving universal weed control, disease-resistant crop high-throughput screening and agricultural product quality control. He has completed research projects supported by institutions from China, Ireland, the European Union (EU) and the United States (US). Based on the creation of new concepts and new technologies, he has achieved a number of innovative research results with over 100 academic papers (google scholor citations about 2000; h-index 26; i 10-index 39) published as the first/corresponding author, of which over 50 papers were published in top journals. He has been invited to give presentations over 30 times in international conferences (such as ASABE Annual International Meeting, CIGR World Congress, IUFoST World Congress, EFFoST International Conference, International Conference on Advanced Vibrational Spectroscopy, International Conference on Near Infrared Spectroscopy) around the world including the US, the UK, Ireland, Denmark, Canada, Spain, Greece, Turkey and the United Arab Emirates (UAE). 


Selected SCI Papers Published as the First/Corresponding Author:

JournalNo. of Papers Pulished
2022 IF 5 Year IFCategory QuartileAuthor Rank
Comprehensive Reviews in Food Science and Food Safety214.817.1
Q11st
Critical Reviews in Food Science and Nutrition110.211.8Q11st
Food Chemistry18.88.6Q11st
Computers and Electronics in Agriculture78.38.3Q11st & Coresponding 
Food Engineering Reviews16.67.1Q11st
Frontiers in Plant Science25.66.8Q1Coresponding 
Smart Cities26.446.1Q11st & Coresponding 
Talanta16.15.4Q11st
Journal of Food Engineering25.55.7Q11st
Biosensors15.45.7Q1Coresponding 
Foods35.25.5Q11st 
Biosystems Engineering35.15.5Q11st & Coresponding 
Remote Sensing35.05.6Q11st & Coresponding 


Social Position

2024-Present, National Natural Science Foundation of China, General Project, Reviewer

2023-Present, Plants (ISSN 2223-7747)，Chief Guest Editor 

2023-Present, China Academic Degrees & Graduate Education Development Center (CDGDC), Expert 

2022-Present, National Research Foundation (NRF) of Singapore,         

   Campus for Research Excellence and Technological Enterprise (CREATE), Peer-Reviewed Expert

2022-Present, Smart Cities (ISSN 2624-6511), Section Editor-in-Chief 

2022-Present, Frontiers in Plant Science (ISSN 1664-462X), Associate Editor

2022-Present, Frontiers in Food Science and Technology (ISSN 2674-1121), Associate Editor

2022-Present, Frontiers in Nutrition (ISSN 2296-861X), Chief Guest Editor

2022-Present, Frontiers in Plant Science (ISSN 1664-462X), Chief Guest Editor

2022-Present, Remote Sensing (ISSN 2072-4292), Chief Guest Editor & Topical Advisory Panel Member

2022-Present, Sensors (ISSN 1424-8220), Chief Guest Editor & Topical Advisory Panel Member

2022-Present, Biosensors (ISSN 2079-6374), Guest Editor

2021-Present, Agriculture (ISSN 2077-0472), Chief Guest Editor & Editorial Board Member

2021-Present, Agronomy (ISSN 2073-4395), Chief Guest Editor & Editorial Board Member

2021-Present, Foods (ISSN 2304-8158), Guest Editor & Topical Advisory Panel Member

2021-Present, Smart Cities (ISSN 2624-6511), Chief Guest Editor & Editorial Board Member

2020, New Faces of ASABE - Professionals, Member 

2018-Present, Artificial Intelligence in Agriculture (ISSN 2589-7217), Editorial Board Member

2018-Present, American Society of Agricultural and Biological Engineers (ASABE), Member

2018-2019, American Society for Horticultural Science (ASHS), Member

International Academic Journals (e.g.Nature Communications, Trends in Food Science and Technology, Food Chemistry, Computers and Electronics in Agriculture, Sensors, Infrared Physics and Technology, IEEE Access, Food Additives and Contaminants, Innovative Food Science and Emerging Technologies, Postharvest Biology and Technology, Drying Technology,etc.), Peer-Reviewed Experts


Dynamic activity

Field

Research field: Intelligent agricultural robots


As a broadly-trained researcher, Prof. Su focuses on the development of advanced sensing and automation technologies for agricultural and biological systems, aiming to embed smart technologies into sustainable agricultural production and management. 


Technical direction: Intelligent equipment for agricultural product production

（1） Intelligent perception and care of animal and plant growth

1. Intelligent perception technology equipment for animal and plant growth

2. Plant recognition & intelligent weeding robot

（2） Intelligent monitoring and control of agricultural product quality

1. Intelligent recognition and monitoring robot for agricultural products

2. Alternative protein production intelligent measurement and control device


Research interests: Smart urban agriculture; Artificial intelligence; Agricultural robotics; Automated control; Unmanned aerial vehicle; Plant phenotyping; Computer vision; Crop plant signaling; Machine (Deep) learning; Food processing and safety; Fluorescence imaging; Hyper/multispectral imaging; UV/Vis/NIR/MIR imaging spectroscopy


For example:

1. Vegetable crop productivity is very susceptible to damage from weed competition. There is an urgent need for a reliable terrestrial sensing system that can work well in a variety of crops to achieve robotic weed control. We are developing a novel technique using a systemic crop signaling compound applied to vegetable seeds and transplants. This technique addresses current challenges that traditional computer vision and machine learning approaches face when attempting to detect and identify crop plants growing in fields with high weed densities and significant levels of foliage occlusion. The crop signaling technique provides the technological breakthrough needed to provide reliable crop and weed differentiation.


2. Crop disease is extensively distributed worldwide to reduce crop yield. Researchers across the world put major effort into breeding for disease resistance. Conventional protocols of phenotyping crop disease severity and selecting for resistance is a costly and time-consuming process. We are now developing high-throughput methods using near-ground and airborne remote sensing coupled with deep learning for crop yield and disease diagnostics.


3. Computer vision has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of foods. Hyperspectral imaging is employed to non-invasively evaluate quality parameters of cereals, fruits, vegetables and meats. In addition to the ability for classifying such foods into different quality grades and gaining the rapid information about their chemical components and physical attributes, imaging spectroscopy with machine learning algorithm is able to determine low levels of pesticide residues in agricultural products, and to detect impurities of specific flour with avoidance of extensive sample preparation. New variable selection methods will be develped and optimized. The proposed feature variables could be used to design on-line multispectral systems for food quality evaluation.



Open Course

Undergraduate Courses: 

1. Principles of Deep Learning, 2023-Present

2. Introduction to Modern Agriculture, 2023-Present

3. Artificial Intelligence Basics, 2022-Present

4. Intelligent Sensing and Information Processing, 2022-Present


Graduate courses: 

1. Deep Learning and Its Agricultural Applications, 2024-Present



Project

1. 2024.08.15-2026.12.31, Science and technology project of provinces. Key technology equipment for grading fresh goji berries using gyroscope effect collaborative image recognition


2. 2024.01.26-2026.08.31, Science and technology project of provinces. Evaluation of plant protein raw materials and development of plant-based solid fats


3. 2023.09.25-2027.12.31, National Natural Science Foundation of China project. Research on multi view detection of tomato canopy fluorescent labeling based on root pathway and precision spraying weed control method


4. 2023.03.28-2025.12.31, National Science and Technology Ministry Project. Construction and Data Mining of Salt tolerant Soybean Phenotype Database


5. 2023.02.17-2025.07.31, Science and technology project of provinces. Research on meat prefabrication and plant-based emerging food safety risk prevention and control technologies


6. 2021.10.30-2024.12.31, National Natural Science Foundation project. Research on the spatiotemporal dynamic perception mechanism and identification of soybean/weed based on systematic crop signal transduction technology


Thesis

Google Scholar

ResearchGate

ORCiD

PEER-REVIEWED JOURNAL ARTICLES AS FIRST OR CORRESPONDING AUTHOR 

TitleJournalCategoryYear

First author affiliation
Real-time lettuce-weed localization and weed severity classification based on lightweight YOLO convolutional neural networks for intelligent intra-row weed controlComputers and Electronics in AgricultureSCI2024China Agricultural University
A novel mechanical-laser collaborative intra-row weeding prototype: structural design and optimization, weeding knife simulation and laser weeding experimentFrontiers in Plant ScienceSCI2024China Agricultural University
High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer VisionPlantsSCI2024

China Agricultural University
Classification, uptake, translocation, and detection methods of nanoparticles in crop plants: a reviewEnvironmental Science: NanoSCI2024China Agricultural University
Steerable Interfacial Assembly of 1D Amyloid-Like Protein Nanocomposites for Enhanced Nanoherbicide UtilizationSmallSCI2024--
YOLOV5-CBAM-C3TR: An optimized model based on transformer module and attention mechanism for apple leaf disease detectionFrontiers in Plant ScienceSCI2024China Agricultural University
APHS-YOLO: A lightweight model for real-time detection and classification of Stropharia rugoso-annulataFoodsSCI2024China Agricultural University
Automatic Localization of Soybean Seedling Based on Crop Signaling and Multi-view ImagingSensorsSCI2024China Agricultural University
The Application of Deep Learning in the whole Potato Production Chain: A Comprehensive ReviewAgricultureSCI2024China Agricultural University
Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A ReviewAgronomySCI2024China Agricultural University
Weed Management Methods for Herbaceous Field Crops: A ReviewAgronomySCI2024China Agricultural University
A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed controlFrontiers in Plant ScienceSCI2023China Agricultural University
Quantitative Evaluation of Maize Emergence Using UAV Imagery and Deep LearningRemote SensingSCI2023China Agricultural University
Full life circle of micro-nano bubbles: Generation, characterization and applicationsChemical Engineering JournalSCI2023--
Synergy between hydrophilic silica nanoparticles and poly (vinylpyrrolidone) for long-term foam stabilization with little energy inputJournal of Molecular LiquidsSCI2023--
Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural NetworkSensorsSCI2023China Agricultural University
Real-Time Recognition and Localization of Apples for Robotic Picking Based on Structural Light and Deep LearningSmart CitiesSCI2023China Agricultural University
A SPH-YOLOv5x-Based Automatic System for Intra-Row Weed Control in LettuceAgronomySCI2023China Agricultural University
An Integrated Multi-Model Fusion System for Automatically Diagnosing the Severity of Wheat Fusarium Head BlightAgricultureSCI2023China Agricultural University
Two-Stage Convolutional Neural Networks for Diagnosing the Severity of Alternaria Leaf Blotch Disease of the Apple TreeRemote SensingSCI2022China Agricultural University
Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A ReviewBiosensorsSCI2022China Agricultural University
Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A ReviewAgronomySCI2022China Agricultural University
Automatic Tandem Dual BlendMask Networks for Severity Assessment of Wheat Fusarium Head BlightAgricultureSCI2022China Agricultural University
SE‐YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and VegetablesAgronomySCI2022China Agricultural University
Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row WeedsAgricultureSCI2022China Agricultural University
Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screeningFood ChemistrySCI2021China Agricultural University
Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer VisionRemote SensingSCI2021China Agricultural University
Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato QualityFoodsSCI2021China Agricultural University
Development of a systemic crop signalling system for automated real-time plant care in vegetable cropsBiosystems EngineeringSCI2020University of California, Davis
Non-destructive evaluation of photostability of crop signaling compounds and dose effects on celery vigor for precision plant identification using computer visionComputers and Electronics in AgricultureSCI2020University of California, Davis
Chemometric determination of time series moisture in both potato and sweet potato tubers during hot air and microwave drying using near/mid-infrared (NIR/MIR) hyperspectral techniquesDrying TechnologySCI2020University College Dublin, National University of Ireland
Crop plant signaling for real-time plant identification in smart farm: A systematic review and new concept in artificial intelligence for automated weed controlArtificial Intelligence in AgricultureSCI2020China Agricultural University
Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A ReviewSmart CitiesSCI2020China Agricultural University
Fluorescence imaging for rapid monitoring of translocation behaviour of systemic markers in snap beans for automated crop/weed discriminationBiosystems EngineeringSCI2019University of California, Davis
Chemometrics in tandem with near infrared (NIR) hyperspectral imaging and Fourier transform mid infrared (FT-MIR) microspectroscopy for variety identification and cooking loss determination of sweet potatoBiosystems EngineeringSCI2019University College Dublin, National University of Ireland
Mid-infrared (MIR) Spectroscopy for Quality Analysis of Liquid FoodsFood Engineering ReviewsSCI2019University College Dublin, National University of Ireland
Automated Identification of Systemic Fluorescent Markers in Vegetable Seedling Leaves for Weed and Crop DifferentiationHortScienceSCI2019University of California, Davis
Rapid Determination of Starch Content of Potato and Sweet Potato by Using NIR Hyperspectral ImagingHortScienceSCI2019University of California, Davis
Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopyJournal of Food Measurement and CharacterizationSCI2019University College Dublin, National University of Ireland
Fingerprinting study of tuber ultimate compressive strength at different microwave drying times using mid-infrared imaging spectroscopyDrying TechnologySCI2019University College Dublin, National University of Ireland
Fourier transform mid-infrared-attenuated total reflectance (FTMIR-ATR) microspectroscopy for determining textural property of microwave baked tuberJournal of Food EngineeringSCI2018University College Dublin, National University of Ireland
Multispectral Imaging for Plant Food Quality Analysis and VisualizationComprehensive Reviews in Food Science and Food SafetySCI2018University College Dublin, National University of Ireland
Fourier Transform Infrared and Raman and Hyperspectral Imaging Techniques for Quality Determinations of Powdery Foods: A ReviewComprehensive Reviews in Food Science and Food SafetySCI2018University College Dublin, National University of Ireland
Non-destructive and rapid evaluation of staple foods quality by using spectroscopic techniques: a reviewCritical Reviews in Food Science and NutritionSCI2017University College Dublin, National University of Ireland
Chemical imaging for measuring the time series variations of tuber dry matter and starch concentrationComputers and Electronics in AgricultureSCI2017University College Dublin, National University of Ireland
Variation analysis in spectral indices of volatile chlorpyrifos and non-volatile imidacloprid in jujube (Ziziphus jujuba Mill.) using near-infrared hyperspectral imaging (NIR-HSI) and gas chromatograph-mass spectrometry (GC–MS)Computers and Electronics in AgricultureSCI2017University College Dublin, National University of Ireland
Evaluation of spectral imaging for inspection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flourJournal of Food EngineeringSCI2017University College Dublin, National University of Ireland
Comparative assessment of feature-wavelength eligibility for measurement of water binding capacity and specific gravity of tuber using diverse spectral indices stemmed from hyperspectral imagesComputers and Electronics in AgricultureSCI2016University College Dublin, National University of Ireland
Multivariate analysis of hyper/multi-spectra for determining volatile compounds and visualizing cooking degree during low-temperature baking of tubersComputers and Electronics in AgricultureSCI2016University College Dublin, National University of Ireland
Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imagingTalantaSCI2016University College Dublin, National University of Ireland
Potential of hyperspectral imaging for visual authentication of sliced organic potatoes from potato and sweet potato tubers and rapid grading of the tubers according to moisture proportionComputers and Electronics in AgricultureSCI2016University College Dublin, National University of Ireland


Achievements

The software works Patent

Software Works

1. Multi variety Huangjing non-destructive identification software based on near-infrared spectroscopy and convolutional neural network, 2024 SR0951686, software copyright registration

2. Crop and weed recognition and segmentation software based on YOLOv8, 2024 SR0891568, software copyright registration

3. Real time detection software for multi nutrient parameters of bacterial protein using near-infrared spectroscopy, 2024, 2024, SR0374245, software copyright registration

4. Software for detecting moisture content in soybean products based on convolutional neural networks, 20232023SR1480117, software copyright registration

5. Protein content detection software for soybean products based on near-infrared spectroscopy technology, 20232023SR1399359, software copyright registration

6. Weed recognition and localization software based on deep learning and geometric algorithms, 20232023SR1304043, software copyright registration

7. A deep learning based software for automatic grading of weed severity in rows, 20232023SR1303914, software copyright registration

8. Apple intelligent recognition and positioning software based on neural network and depth camera, 20232023SR0699324, software copyright registration

9. Portable intelligent diagnosis software for wheat Fusarium head blight disease level based on mobile APP, 20232023SR0289635, software copyright registration

10. Lettuce root and stem position localization software based on image processing, 20222022SR1080305, software copyright registration

11. Lettuce and Weed Software Based on YOLOv5x, 20222022SR1069303, Software Copyright Registration

12. Semi finished shrimp length detection software based on deep learning and image processing technology, 20222022SR0856892, software copyright registration

13. Semi finished shrimp body area detection software based on convolutional neural network, 20222022SR0856891, software copyright registration

14. Automatic counting software for plant seedlings based on machine vision, 20222022SR0494748, software copyright registration

15. Crop and weed detection software based on plant signal technology, 20222022SR0494747, software copyright registration

16. Apple Leaf Segmentation Software Based on Deep Learning, 20222022SR0494665, Software Copyright Registration

17. Apple Spot and Leaf Disease Spot Segmentation Software Based on Deep Learning, 20222022SR0494664, Software Copyright Registration


patent

1. A real-time mechanical weeding method and equipment within the industry, 20212021202111534202.0


Honor

1. 2024, The second prize instructor for the North China division of the 19th National College Student Intelligent Vehicle Competition

2. 2024, The third prize instructor for the Beijing division of the China International College Student Innovation Competition

3. 2024, Project Leader of Key R&D Program in Ningxia Autonomous Region

4. 2024, Outstanding Graduate Guidance Teacher at the Beijing Municipal Level for Graduate Students

5. 2024, Outstanding Graduate Guidance Teacher for Undergraduate Students at the Beijing Municipal Level

6. 2024, The advisor for one hundred outstanding undergraduate theses at China Agricultural University

7. 2024, The second prize instructor for the 10th Beijing College Student Biology Creative Ideas Competition

8. 2024, National College Student Innovation and Entrepreneurship Training Program Innovation Project Guidance Teacher

9. 2023, The guiding teacher for the key project of the Independent Innovation Research Fund for Graduate Students at China Agricultural University

10. 2023, Master's National Scholarship Guidance Teacher

11. 2023, National Natural Science Foundation of China General Project Leader

12. 2023, Beijing Science and Technology Commissioner

13. 2023, Graduate supervisor for the cross disciplinary talent cultivation program at China Agricultural University

14. 2022, National Key R&D Program Project Leader

15. 2022, The Organization Department of the Beijing Municipal Party Committee selected talents for the Talent Beijing Suburban Tour

16. 2022, Beijing Innovation Team Award for Modern Agricultural Industry Technology System

17. 2021, Shennong China Agricultural Science and Technology Award Outstanding Innovation Team Award

18. 2021, National Natural Science Foundation of China Youth Project Leader

19. 2021, Guidance Teacher for the Graduate Independent Innovation Research Fund Project at China Agricultural University


Enrollment

Our team has the National Agricultural Product Processing Technology and Equipment Research and Development Center of the Ministry of Agriculture and Rural Affairs, as well as the National Non destructive Evaluation and Identification Instrument and Equipment Technology Beijing Center for Famous and Excellent New Agricultural Products of the Ministry of Agriculture and Rural Affairs. The team has been rated as a high-level innovation team of China Agricultural University, a Beijing innovation team of modern agricultural industry technology system, and an excellent innovation team of the Ministry of Agriculture and Rural Affairs. Due to the practical needs of scientific research, there is an urgent need to recruit several outstanding agricultural engineering (including agricultural engineering, agricultural mechanization and automation, agricultural intelligent equipment engineering), mechanical engineering (vehicle engineering, mechanical and electronic engineering, mechanical manufacturing and automation, mechanical design and theory) or other related majors (including artificial intelligence, electronic information, computer science, biological food, plant science) postdoctoral/graduate students (including doctoral and master's degrees) and undergraduate students in innovation and entrepreneurship projects.


Postdoctoral recruitment (1 position):

1. Obtained relevant professional doctoral degrees from well-known universities or research institutions at home and abroad in the past three years;

2. Has strong technological innovation and English writing and expression skills, and has published high-level academic papers as the first author in international academic journals;

3.  Under the age of 35;

4. Candidates with backgrounds in intelligent sensing, computer vision, automatic control equipment development, software development, and image processing are given priority consideration.


Graduate enrollment (4-7 students/year):

We are recruiting 1 PhD candidate, 1-2 Master's degree candidates, and 2-4 Professional Master's degree candidates. We welcome applications from students majoring in related fields.




Enrollment in previous years Application intention