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

Recent Publications

(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 (in press).

(2021). Hyperspectral Imaging Combined with Machine Learning for the Detection of Fusiform Rust Disease Incidence in Loblolly Pine Seedlings. Remote Sensing 13(18), 3595.

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(2021). Opportunities for Robotic Systems and Automation in Cotton Production. AgriEngineering 2021, 3(2), 339-362.

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(2021). Using Hyperspectral Imaging for Differentiating Cultivars, Growth Stages, Flowers and Leaves of Industrial Hemp. Industrial Crops and Products (under review).

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

PDF DOI

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

(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

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

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

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

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(2020). Technology progress in mechanical harvest of fresh market apples. Computers and Electronics in Agriculture, 175, 105606.

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(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.

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(2020). High Throughput Phenotyping for Fusiform Rust Disease Resistance in Loblolly Pine Using Hyperspectral Imaging. 2020 ASABE Annual International Virtual Meeting, 2000872.

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(2020). Hyperspectral Imaging-Enabled High-Throughput Screening of Loblolly Pine (Pinus taeda) Seedlings for Freeze Tolerance. 2020 ASABE Annual International Virtual Meeting, 2001072.

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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)

Openings

I am actively looking for a MS/PhD graduate student to start in Fall 2021 or Spring 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 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.

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