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

Grad Students

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Ebenezer Olaniyi

PhD Student

Projects

Nondestructive Detection of Spotted Wing Drosophila Infestation in Blueberry Fruit Using Optical Imaging (2021-2023, USDA-AMS Specialty Crop Block Grant Program)

Spotted Wing Drosophila (SWD) is a devasting pest for blueberries grown in Mississippi and beyond. Blueberry growers are under intense pressure to monitor SWD infestation and protect yield loss. The zero tolerance for SWD larvae inside the fruit is also posing significant challenges for packinghouses in identifying and removing infested fruit to ensure safe fruit to consumers.

Utilize Machine Vision Technology to Improve Efficiency and Reduce Labor of Local Sweetpotato Packing Lines (2021-2023, USDA-AMS Specialty Crop Block Grant Program)

In this project, we will ollaborate with local sweetpotato packinghouses to enhance the efficiency and reduce human labor of packing operations by: 1) assessing the status of automation technology utilized in local sweetpotato packing lines and 2) designing and evaluating a machine vision technology-based inspection system for automatically grading and sorting sweetpotatoes for size, shape and defects.

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

Y. Lu*, S. Young (2021). Robust Plant Segmentation of Color Images Based on Image Contrast Optimization. Computers and Electronics in Agriculture (under review).

D. Chen, Y. Lu*, Z. Li, S. Young (2021). Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems. Computers and Electronics in Agriculture (in preparation).

Yuzhen Lu, K.G. Payn, P. Pandey, J.J. Acosta, P. Pandey, A.J. Heine, T.D. Walker, S. Young (2021). Hyperspectral Imaging with cost-sensitive learning for high-throughput screening of loblolly pine (Pinus Taeda L.) seedling for freeze tolerance. Transactions of the ASABE.

PDF DOI

P. Pandey, K.G. Payn, Yuzhen Lu, A.J. Heine, T.D. Walker, J.J. Acosta, S. Young (2021). Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings. Remote Sensing 13(18), 3595.

PDF DOI

E. Barnes, G. Morgan, J. Devine, R. Kurtz, G. Ibendahl, A. Sharda, G. Rains, J. Snider, J. Mari Maja, J.A. Thomasson, Yuzhen Lu, H. Gharakhani, J. Griffin, E. Kimura, R. Hardin, T. Raper, S. Young, K. Fue, M. Pelletier, J. Wanjura, G. Holt (2021). Opportunities for Robotic Systems and Automation in Cotton Production. AgriEngineering 2021, 3(2), 339-362.

PDF DOI

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 (under review).

Yuzhen Lu, R. Lu, Z. Zhang (2021). Detection of subsurface bruising in fresh pickling cucumbers using structured-illumination reflectance imaging. Postharvest Biology and Technology 180, 111624.

PDF DOI

Yuzhen Lu, R. Lu (2021). Detection of Chilling Injury in Pickling Cucumbers Using Dual-band Chlorophyll Fluorescence Imaging. Foods 10(5), 1094.

PDF DOI

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 67(3), 321–334.

PDF DOI

Z. Zhang, Yuzhen Lu, R. Lu (2021). Development and evaluation of an apple infield grading and sorting system. Postharvet Biology and Technology 180, 111588.

PDF DOI

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, 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: ABE 4990/6990 Introduction to Imaging in Biological Systems

2022 Fall: Problem Solving in Agricultural and Biological Engineering (to be developed)

News

06/2021, Dr. Lu joined Fronteris in Plant Science as an Associate Editor 👋👋

Jun 26, 2021 0 min read

05/2021, Dr. Lu joined the Youth Editorial Board of the journal Smart Agriculture 👋👋

Last updated on May 26, 2021 0 min read

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
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Openings

I am actively looking for a MS/PhD graduate student to start in Spring or Fall 2022 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 robotics 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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