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 pressing problems in the agricultural and food domain.

Before assuming my current position at Mississippi State University, I was a Postdoc Research Associate for USDA-ARS at East Lansing, Michigan, on optical imaging for quality evaluation of horticultural products, and apple harvest-assist and in-field sorting technology development, and subsequently joined in the Department of Biological and Agricultural Engineering at North Carolina State University working on several projects on imaging-based high-throughput plant phenotyping and precision agriculture.

Starting on 1/1/2023, our lab will move to the Department of Biosystems and Agricultural Engineering at Michigan State University. GO GREEN!

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

Postdocs

Jiajun Xu

Graduate Assistants

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Ebenezer Olaniyi

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Jiaxu Cai

Vahid Farzand Ahmadi

Undergraduate Assistants

Gracie Cockerham

Joseph Coker

Timothy Usey

Zijie Chen

Projects

Instrumented Assessment of Sweetpotato Harvest and Packing Line Impacts Towards Damage Reduction (2022-2024, USDA-AMS Specialty Crop Block Grant Program)

In his project, we will ollaborate with local sweetpotato growers and packers to reduce mechnical impact during harvest and packing line operations by: 1) measuring impacts due to mechanical harvest and packing lines using an impact recording device, 2) quantifying visual damage on sweetpotato storage roots through image analysis, and 3) determining sweetpotato impact damage thresholds.

Advancing Optical Technologies for Enhanced Quality Evaluation, Grading and Sorting of Sweetpotatoes (2022-2025, USDA-AMS, Specialty Crop Multi-State Program)

This multi-state project aims to advance non-destructive optical technologies for enhanced quality evaluation, grading and sorting of sweetpotatoes. Specifically, this project will: develop computer algorithms for quantification and classification of appearance quality of sweetpotatoes using machine vision technology; evaluate hyperspectral and interactance imaging for detecting internal quality, including firmness, soluble solids, dry matter, and internal defects of sweetpotatoes; develop and evaluate an automated sweetpotato grading and sorting machine prototype.

Develop image databases to support machine vision-based cotton weed control and harvest and deploy deep learning models for weed detection towards precision weeding (2022, Cotton Inc., Cary, NC)

This proposed project aims to develop and deploy deep learning models for weed detection for cotton production systems. We will pursue three sub-objectives: 1) Collect and label new images of weed species in cotton fields across Mississippi and integrate images supporting robotic cotton harvest systems (mature bolls); 2) Develop and evaluate deep learning models for weed detection; 3) Deploy deep learning models on a machine vision prototype for in-field weed detection.

Sinusoidal-Illumination Imaging (SII) A Potential Tool for Enhanced Detection of Muscle Defects and Microbial Spoilage of Poultry (2022-2023, USDA-NIFA)

The prevalence of muscle defects in broiler meat, such as white striping, woody breast and spaghetti meat, is threating the profitability and sustainability of the U.S. poultry industry. Poultry products are also highly susceptible to microbial spoilage or contamination, leading to substantial product and economic loss and potential food safety issues.

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 (2021, 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

Ebenezer Olaniyi, Yuzhen Lu, Jiaxu Cai, Anuraj Theradiyil Sukumaran, T. Jarvis (2022). Feasibility of Imaging under Structured Illumination for Evaluation of White Striping in Broiler Breast Fillets. Journal of Food Engineering 342, 111359.

PDF DOI

Dong Chen, Xinda Qi, Yu Zheng, Yuzhen Lu, Zhaojian Li (2022). Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach. Computers and Electronics in Agriculture (in preparation).

PDF

Jianbo Li, Yuzhen Lu, Renfu Lu (2022). Detection of early decay in navel oranges by structured-illumination reflectance imaging combined with image enhancement and segmentation. Postharvest Biology and Technology 196,112162.

PDF DOI

Hussein Gharakhani, J. Alex Thomasson, Yuzhen Lu (2022). Integration and Preliminary Evaluation of A Robotic Cotton Harvester Prototype. Computers and Electronics in Agriculture (under review).

Jiajun Xu, Byeong-Geon Kim, Yuzhen Lu, Kyoung-Su Park (2022). Optimal and Smooth Path Planning for Mobile Cable-Driven Parallel Robots in Highly Constrained Environment. IEEE Transactions on Mechatronics (under review).

W. Liu, J. Lyu, D. Wu, Y. Cao, Q. Ma, Yuzhen Lu, X. Zhang (2022). Cutting techniques in the fish industry: a critical review. Foods 11(20), 3206.

PDF DOI

A. Rahman, Yuzhen Lu, H. Wang (2022). Deep Object Detectors for Detecting Weeds for Precision Weed Control. Smart Agricultural Technology 100126.

PDF Code Dataset DOI

Yuzhen Lu, X. Li, X. Li, S. Young, E. Linder, D. Suchoff (2022). Hyperspectral Imaging with Chemometrics for Non-destructive Determination of Cannabinoids in Floral and Leaf Materials of Industrial Hemp. Computers and Electronics in Agriculture 202, 107387.

PDF Code DOI

Yuzhen Lu, Jiaxu Cai (2022). Structured-Light Imaging. In Q. Zhang (Ed.) Encyclopedia of Smart Agriculture Technologies (submitted). Springer.

F. Dang, D. Chen, Y. Lu*, Z. Li (2022). YOLOWeeds: A Novel Benchmark of YOLO Object Detectors for Weed Detection in Cotton Production Systems. Computers and Electronics in Agriculture (under review).

D. Chen, Y. Lu*, Z. Li, S. Young (2022). Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems. Computers and Electronics in Agriculture 198, 107091.

PDF Code Dataset

Hussein Gharakhani, J. Alex Thomasson, Yuzhen Lu (2022). An End-Effector for Robotic Cotton Harvesting. Smart Agricultural Technology 2, 100043.

PDF DOI

Yuzhen Lu, Z. Zhang, R. Lu (2022). Development and Preliminary Evaluation of A New Apple Harvest-Assist and In-field Sorting Machine. Appled Engineering in Agriculture 38(1), 23-35.

PDF DOI

Yuzhen Lu, Sierra Young (2022). Robust Plant Segmentation of Color Images Based on Image Contrast Optimization. Computers and Electronics in Agriculture 193, 106711.

PDF DOI

Yuzhen Lu, S. Young, E. Linder, D. Suchoff (2022). Using Hyperspectral Imaging for Differentiating Cultivars, Growth Stages, Flowers and Leaves of Industrial Hemp. Frontiers in Plant Science 12, 810113.

PDF DOI

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 64(6): 2045-2059.

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

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

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

PDF DOI

See all publications

Teaching

Spring Semester: ABE 4423/6423 Bioinstrumentation II

Fall Semester: ABE 4990/6990 Introduction to Imaging in Biological Systems

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

News

Our poultry quality imaging research, funded by MSU-MAFES and USDA-NIFA, is making headlines on campus and the WATTPoultry website 👋👋.

Read the reports here: https://www.msstate.edu/newsroom/article/2022/09/making-grade-msu-scientists-apply-high-resolution-imaging-technology and https://www.wattagnet.com/articles/46075-fruit-imaging-approach-could-detect-poultry-meat-myopathies
Last updated on Nov 12, 2022 0 min read

In Janurary of 2023, our lab will move to the Department of Biosystems & Agricultural Engineering at Michigan State University 👋👋

Last updated on Aug 31, 2022 0 min read

10/2021, Dr. Lu joined the ASABE PRS Community as an Associate Editor 👋👋

Last updated on Oct 22, 2021 0 min read

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
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Openings

I am actively looking for a MS/PhD graduate student to start in Spring/Fall 2023 in the Department of Biosystems & Agricultural Engineering at Michigan 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 GRA position (in English). See the GRA position (in Chinese).

In additon, I also have a Postdoc position focusing on real-time computer vision, grading, and sorting for specialty crop products. The position can be started in Fall 2022 or Spring 2023, for two years of funding (with possibility of extension). Qualifications for this position include demonstrated experience in computer vision, sensors and control, software-hardware integration, and strong publication records in agricultural engineering journals.

See the Postdoc position .

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