Wen-Hao Su

Prof. Wen-Hao Su is a Principal Investigator (PI) 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. 

Prof. Su is also the Chief Engineer of the Administrative Committees of Zhongguancun Science Park (Pinggu, Beijing). He has worked at College of Engineering, China Agricultural University since 2020. 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. 

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 90 academic papers (google scholor citations about 1600; h-index 23; i 10-index 34) published as the first/corresponding author, of which 37 papers were published in top journals (IF 260+). 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:

Journal	No. of Papers Pulished	2022 IF	5 Year IF	Category Quartile	Author Rank
Comprehensive Reviews in Food Science and Food Safety	2	14.8	17.1	Q1	1st
Critical Reviews in Food Science and Nutrition	1	10.2	11.8	Q1	1st
Food Chemistry	1	8.8	8.6	Q1	1st
Computers and Electronics in Agriculture	6	8.3	8.3	Q1	1st & Coresponding
Food Engineering Reviews	1	6.6	7.1	Q1	1st
Frontiers in Plant Science	2	5.6	6.8	Q1	Coresponding
Smart Cities	2	6.44	6.1	Q1	1st & Coresponding
Talanta	1	6.1	5.4	Q1	1st
Journal of Food Engineering	2	5.5	5.7	Q1	1st
Biosensors	1	5.4	5.7	Q1	Coresponding
Foods	1	5.2	5.5	Q1	1st
Biosystems Engineering	3	5.1	5.5	Q1	1st & Coresponding
Remote Sensing	3	5.0	5.6	Q1	1st & Coresponding

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

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. 

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

Undergraduate Courses: 

1. Artificial Intelligence Basics, 2022-2023, Semester 2, Friday, East Campus 

2. Intelligent Sensing and Information Processing, 2022-2023, Semester 1, Tuesday & Thursday, East Campus 

1. 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.

2. 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.

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.

Google Scholar

PEER-REVIEWED JOURNAL ARTICLES

·  J.-L. Li, Wen-Hao Su^*, H.-Y. Zhang, Y. Peng. A real-time smart sensing system for automatic localization and recognition of vegetable plants for weed control. Frontiers in Plant Science, 2023, 14, 1-12.

·  M. Liu^*, Wen-Hao Su^*, X.-Q. Wang. Quantitative Evaluation of Maize Emergence Using UAV Imagery and Deep Learning. Remote Sensing, 2023, 15(8), 1979.

·  K.-J. Fan, B.-Y. Liu, Wen-Hao Su^*. Discrimination of Deoxynivalenol Levels of Barley Kernels Using Hyperspectral Imaging in Tandem with Optimized Convolutional Neural Network. Sensors, 2023, 23(5), 2668.

·  Y.-H. Wang, Wen-Hao Su^*. Convolutional Neural Networks in Computer Vision for Grain Crop Phenotyping: A Review. Agronomy, 2022, 12, 2659.

·  Y. Gao, H. Wang, M. Li, Wen-Hao Su^*. Automatic Tandem Dual BlendMask Networks for Severity Assessment of Wheat Fusarium Head Blight. Agriculture, 2022, 12(9), 1493.

·  J.-L. Zhang, Wen-Hao Su^*, H.-Y. Zhang, Y. Peng. SE‐YOLOv5x: An Optimized Model Based on Transfer Learning and Visual Attention Mechanism for Identifying and Localizing Weeds and Vegetables. Agronomy, 2022, 12(9), 2061.

·  B-Y Liu, K.-J. Fan, Wen-Hao Su^*, Y. Peng. Two-Stage Convolutional Neural Networks for Diagnosing the Severity of Alternaria Leaf Blotch Disease of the Apple Tree. Remote Sensing, 2022, 14(11), 2519.

·  Wen-Hao Su^*, J. Sheng, Q.-Y. Huang. Development of a Three-Dimensional Plant Localization Technique for Automatic Differentiation of Soybean from Intra-Row Weeds. Agriculture, 2022, 12, 195.

·  K.-J. Fan, Wen-Hao Su^*. Applications of Fluorescence Spectroscopy, RGB- and MultiSpectral Imaging for Quality Determinations of White Meat: A Review. Biosensors, 2022, 12, 76.

· Wen-Hao Su, H. Xue. Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality. Foods, 2021, 10(9), 2146.

· Wen-Hao Su^*. Rapid Softness Prediction and Microbial Spoilage Visualization of Whole Tomatoes by Using Hyper/Multispectral Imaging. Challenges, 2021, 12(2), 21.

· Wen-Hao Su, J. Zhang, C. Yang, R. Page, T. Szinyei, C.D. Hirsch, B.J.Steffenson. Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sensing, 2021, 13(1), 26.

· Wen-Hao Su, C. Yang, Y. Dong, R. Johnson, R. Page, T. Szinyei, C.D. Hirsch,  B.J. Steffenson. Hyperspectral imaging and improved feature variable selection for automated determination of deoxynivalenol in various genetic lines of barley kernels for resistance screening. Food Chemistry, 2021,343, 128507.

· Wen-Hao Su^*, Steven A. Fennimore, David C. Slaughter. Development of a systemic crop signalling system for automated plant care in vegetable crops. Biosystems Engineering, 2020, 193, 62-74.

· Wen-Hao Su^*, David C. Slaughter, Steven A. Fennimore. Non-destructive evaluation of photostability of crop signaling compounds and dose effects on celery vigor for precision plant identification using computer vision. Computers and Electronics in Agriculture, 2020, 168, 105155.

· Wen-Hao Su^*. Crop plant signaling for real-time plant identification in smart farm: A systematic review and new concept in artificial intelligence for automated weed control. Artificial Intelligence in Agriculture, 2020, 4, 262-271.

· Wen-Hao Su^*. Advanced Machine Learning in Point Spectroscopy, RGB-and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review. Smart Cities,2020, 3 (3), 767-792.

· Wen-Hao Su^*. Systemic Crop Signaling for Automatic Recognition of Transplanted Lettuce and Tomato under Different Levels of Sunlight for Early Season Weed Control. Challenges,2020, 11(2), 1-13.

· Wen-Hao Su, Serafim Bakalis, Da-Wen Sun. 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 techniques. Drying Technology, 2020, 38, 806-823.

· Wen-Hao Su^*, Steven A. Fennimore, David C. Slaughter. Fluorescence imaging for rapid monitoring of translocation behaviour of systemic markers in snap beans for automated crop/weed discrimination. Biosystems Engineering, 2020, 186, 156-167.

·Wen-Hao Su, Da-Wen Sun. Mid-infrared (MIR) spectroscopy for quality analysis of liquid foods. Food Engineering Reviews, 2019, 11(3), 142-158.

· Wen-Hao Su, Serafim Bakalis & Da-Wen Sun. 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 potato. Biosystems Engineering, 2019, 180, 70-86.

· Wen-Hao Su, Serafim Bakalis, Da-Wen Sun. Fingerprinting study of tuber ultimate compressive strength (UCS) at different microwave drying times using mid-infrared (MIR) imaging spectroscopy. Drying Technology, 2019,37(9), 1113-1130.

· Wen-Hao Su, Serafim Bakalis, Da-Wen Sun. Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy.Journal of Food Measurement and Characterization, 2019, 13(2), 1218-1231.

· Wen-Hao Su, Da-Wen Sun. Multispectral Imaging for plant food quality analysis and visualization. Comprehensive Reviews in Food Science and Food Safety, 2018, 17, 220-239.

· Wen-Hao Su, Da-Wen Sun. Fourier transform infrared and Raman and hyperspectral imaging techniques for quality determinations of powdery foods: a review. Comprehensive Reviews in Food Science and Food Safety, 2018, 17, 104-122.

·Wen-Hao Su, Serafim Bakalis, Da-Wen Sun. Fourier transform mid-infrared-attenuated total reflectance (FTMIR-ATR) microspectroscopy for determining textural property of microwave baked tuber. Journal of Food Engineering, 2018, 218, 1-13.

· Wen-Hao Su, Hong-Ju He, Da-Wen Sun. Non-destructive and Rapid Evaluation of Staple Foods Quality by Using Spectroscopic Techniques: A Review. Critical Reviews in Food Science and Nutrition, 2017, 57, 1039-1051.

· Wen-Hao Su, Da-Wen Sun. Evaluation of spectral imaging for detection of adulterants in terms of common wheat flour, cassava flour and corn flour in organic Avatar wheat (Triticum spp.) flour. Journal of Food Engineering, 2017, 200, 59-69.

· Wen-Hao Su, Da-Wen Sun. Chemical imaging for measuring the time series variations of tuber dry matter and starch concentration. Computers and Electronics in Agriculture, 2017, 140, 361-373.

· Wen-Hao Su, Da-Wen Sun. 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 Agriculture, 2017, 139, 41-55.

· Wen-Hao Su, Da-Wen Sun. Comparative assessment of feature-wavelength eligibility for measurement of water binding capacity and specific gravity of tuber using diverse spectral indices stemmed from hyperspectral images. Computers and Electronics in Agriculture, 2016, 130, 69-82.

· Wen-Hao Su, Da-Wen Sun. Facilitated wavelength selection and model development for rapid determination of the purity of organic spelt (Triticum spelta L.) flour using spectral imaging. Talanta, 2016, 155, 347-357.

· Wen-Hao Su, Da-Wen Sun. Multivariate analysis of hyper/multi spectra for determining volatile compounds and visualizing cooking degree during low-temperature baking of tubers. Computers and Electronics in Agriculture, 2016, 127, 561-571.

· Wen-Hao Su, Da-Wen Sun. 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 proportion. Computers and Electronics in Agriculture, 2016, 125, 113-124.

BOOK CHAPTERS

· Wen-Hao Su, C. Yang, Y. Dong, R. Johnson, R. Page, T. Szinyei, C.D. Hirsch,  B.J. Steffenson, Chapter 5-Hyperspectral Imaging and Machine Learning for Rapid Assessment of Deoxynivalenol of Barley Kernels, Nondestructive Evaluation of Agro-products by Intelligent Sensing Techniques, pp. 120-137, Bentham Books / Bentham Science Publishers, Singapore, 2021.

· Wen-Hao Su, Da-Wen Sun, Chapter 5-Advanced Analysis of Roots and Tubers by Hyperspectral Techniques, Advances in Food and Nutrition Research, Vol. 87, pp. 255-303, Academic Press / Elsevier, San Diego, California, USA, 2019.

· Wen-Hao Su, I. S. Arvanitoyannis, Da-Wen Sun, Chapter 18-Trends in Food Authentication, Modern Techniques for Food Authentication, 2nd Edition, pp. 731-758, Academic Press / Elsevier, San Diego, California, USA, 2018.

The software works Patent

1. Beijing Innovation Team Award for Modern Agricultural Industrial Technology System, 2022

2. Excellent Innovation Team Award of Shennong China Agricultural Science and Technology Award, 2021 

3. Excellent talents of China Agricultural University, 2020

4. New Faces of ASABE - Professionals, 2020

5. Best Paper Award, 2018