Yuzhen Lu

Yuzhen Lu

Assistant Professor

Mississippi State University

I am an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. My research resolves around the development and deployment of non-destructive sensing (e.g., optical imaging and spectroscopy) and automation technology for addressing the pressing problems in the agricultural and food domain. After earning my doctorate, I worked for over 1 year for USDA-ARS at East Lansing, Michigan, on optical imaging for qualty evaluation of horticultural products, and apple harvest-assist and in-field sorting technology development, and later spent another year in the Department of Biological and Agricultural Engineering at North Carolina State University on several projects on imaging-based high-throughput plant phenotyping and precision agriculture.

Interests

  • Optical Sensing
  • Machine Vision
  • Food Inspection
  • Precision Agriculture
  • Plant Phenotyping
  • AI & Robotics

Education

  • PhD in Biosystems Engineering, 2018

    Michigan State University

  • MS in Plant Nutrition, 2014

    University of Chinese Academy of Sciences

  • BS in Facility Agriculture Engineering, 2011

    Northwest A&F University

Team

Principal Investigator

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Yuzhen Lu

Assistant Professor

Projects

Cotton weed database development and deep learning model benchmarking ($30,000 for 2021, funded by Cotton Inc., Cary, NC)

This research is to develop a large-scale, public image database of cotton weeds in the field and benchmark deep learning models for weed detection, with the ultimate goal to enhance computer vision-based robotic weeding for cotton.

Recent Publications

Yuzhen Lu, S. Young, E. Linder, P. Pandey, D. Suchoff (2021). Using Hyperspectral Imaging for Differentiating Cultivars, Growth Stages, Flowers and Leaves of Industrial Hemp. Industrial Crops and Products (submitted to journal).

Yuzhen Lu, R. Lu, Z. Zhang (2021). Detection of subsurface bruising in fresh pickling cucumbers using structured-illumination reflectance imagings. Postharvest Biology and Technology (under review).

Yuzhen Lu, S. Young (2021). A New Method for Robust Plant Segmentation Based on Contrast Enhancement and Automatic Thresholding. Transactions of the ASABE (in preparation).

Yuzhen Lu, R. Lu (2021). Detection of Chilling Injury in Pickling Cucumbers Using Dual-band Chlorophyll Fluorescence Imaging. Foods (under review).

Z. Zhang, Yuzhen Lu, R. Lu (2021). Development and evaluation of an apple infield grading and sorting system. Postharvet Biology and Technology (under review).

Yuzhen Lu, Z. Zhang, R. Lu (2021). Development and Preliminary Evaluation of A New Apple Harvest-Assist and In-field Sorting Machine. Appled Engineering in Agriculture (under review).

Yuzhen Lu, K.G. Payn, P. Pandey, J.J. Acosta, P. Pandey, A.J. Heine, T.D. Walker, S. Young (2021). Hyperspectral Imaging-Enabled High-Throughput Screening of Loblolly Pine (Pinus taeda L.) Seedlings for Freeze Tolerance. Frontiers in Plant Science (under review).

Yuzhen Lu, T.D. Walker, J.J. Acosta, S. Young, P. Pandey, A.J. Heine, K.G. Payn (2021). Prediction of Freeze Damage and Minimum Winter Temperature of the Seed Source of Loblolly Pine Seedlings Using Hyperspectral Imaging. Forest Science (in press).

PDF DOI

Yuzhen Lu, S. Young (2020). A Survey of Public Datasets for Computer Vision Tasks in Precision Agriculture. Computers and Electronics in Agriculture 178, 105760.

PDF DOI

Z. Zhang, C. Igathinathane, J. Li, H. Cen, Yuzhen Lu, P. Flores (2020). Technology progress in mechanical harvest of fresh market apples. Computers and Electronics in Agriculture, 175, 105606.

PDF DOI

Yuzhen Lu, W. Saeys, M. Kim, Y. Peng, R. Lu (2020). Hyperspectral imaging technology for quality and safety evaluation of horticultural products: A review and celebration of the past 20-year progress. Postharvest Biology and Technology, 170, 111318.

PDF DOI

Yuzhen Lu, R. Lu (2020). siritool: A Matlab Graphical User Interface for Image Analysis in Structured-Illumination Reflectance Imaging for Fruit Defect Detection. Transactions of the ASABE, 63(4): 1037-1047.

PDF DOI

P. Pandey, K.G. Payn, Yuzhen Lu, A.J. Heine, T.D. Walker, S. Young (2020). High Throughput Phenotyping for Fusiform Rust Disease Resistance in Loblolly Pine Using Hyperspectral Imaging. 2020 ASABE Annual International Virtual Meeting, 2000872.

PDF DOI

Yuzhen Lu, K.G. Payn, P. Pandey, J.J. Acosta, A.J. Heine, T.D. Walker, S. Young (2020). Hyperspectral Imaging-Enabled High-Throughput Screening of Loblolly Pine (Pinus taeda) Seedlings for Freeze Tolerance. 2020 ASABE Annual International Virtual Meeting, 2001072.

PDF DOI

Yuzhen Lu, R. Lu (2020). Enhancing chlorophyll fluorescence imaging under structured illumination with automatic vignetting correction for detection of chilling injury in cucumbers. Computers and Electronics in Agriculture, 168, 105145.

PDF DOI

Teaching

2021 Spring: ABE 4423 Bioinstrum II (co-instructed)

2021 Fall: Problem Solving in Agricultural and Biological Engineering (to be developed)

2021 Fall: Introduction to Imaging in Agriculture (to be developed)

News

03/2021, Dr. Lu joined the ASABE ITSC Editorial Team as an Associate Editor for ASABE Journals 👋👋

Last updated on Mar 24, 2021 0 min read

11/2020, Dr. Lu joined Mississippi State University as an Assistant Professor 👋 👋

Dec 18, 2020 0 min read

07/2020, Dr. Lu received the ASABE Outstanding Reviewer Award for 2019 👋👋

Last updated on Dec 18, 2020 0 min read

03/2020, Our infield sorting demo was released by USDA/ARS on YouTube

Last updated on Dec 18, 2020 0 min read
03/2020, Our infield sorting demo was released by USDA/ARS on YouTube

01/2020, Our apple harvest-assist and infield sorting machine was reported by the Good Fruit Grower magazine

Last updated on Dec 18, 2020 0 min read
01/2020, Our apple harvest-assist and infield sorting machine was reported by the Good Fruit Grower magazine
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Openings

I am looking for a MS-level (Ph.D. possible) graduate student to start in Spring or Summer 2021 in the Department of Agricultural and Biological Engineering at Mississippi State University. The student will be expected to conduct original research within the fields of sensing and automation for agriculture-food systems. Potential research topics include but are not limited to sensing and assessment of food quality and safety, imaging-based plant phenotpyping, and artificial intelligence and IoT for precision plant/animal production. Students with engineering backgrounds (e.g., agricultural/food engineering, electrical engineering, computer science, etc.) and strong experience in computer vision and machine learning are highly welcome to apply. Please email me (yzlu@abe.msstate.edu, luyuzhen@msu.edu) with your CV if you are interested.

See the position.

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