Yuzhen Lu

Yuzhen Lu

Assistant Professor

Michigan State University

I am an Assistant Professor in the Department of Biosystems and Agricultural Engineering at Michigan State University (MSU). 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 joining the faculty at MSU, I was an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University during 2020-2022. I did my postdoctoral research on optical imaging for quality evaluation of horticultural products, and apple harvest-assist and in-field sorting technology development when with USDA-ARS at East Lansing, MI, and worked on several projects on imaging-based high-throughput plant phenotyping and precision agriculture while in the Department of Biological and Agricultural Engineering at North Carolina State University.


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


  • 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


Principal Investigator


Yuzhen Lu

Assistant Professor


Graduate Assistants

Undergraduate Assistants


Recent Publications

(2023). Survey and cost-benefit analysis of sorting technology for the sweetpotato packing lines. AgriEngineering 5(2), 941-949.


(2023). Integration and Preliminary Evaluation of A Robotic Cotton Harvester Prototype. Computers and Electronics in Agriculture (in press).

(2023). Efects of fne grinding on mid‑infrared spectroscopic analysis of plant leaf nutrient content. Scientific Report 13, 6314.


(2023). Structured-Light Imaging. In Q. Zhang (Ed.) Encyclopedia of Smart Agriculture Technologies. Springer, Cham.


(2023). YOLOWeeds: A Novel Benchmark of YOLO Object Detectors for Weed Detection in Cotton Production Systems. Computers and Electronics in Agriculture 205, 107655.

PDF Code Dataset DOI

(2022). Feasibility of Imaging under Structured Illumination for Evaluation of White Striping in Broiler Breast Fillets. Journal of Food Engineering 342, 111359.


(2022). Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach. Computers and Electronics in Agriculture (in preparation).


(2022). Optimal and Smooth Path Planning for Mobile Cable-Driven Parallel Robots in Highly Constrained Environment. IEEE Transactions on Mechatronics (under review).

(2022). Cutting techniques in the fish industry: a critical review. Foods 11(20), 3206.


(2022). Deep Object Detectors for Detecting Weeds for Precision Weed Control. Smart Agricultural Technology 100126.

PDF Code Dataset DOI

(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.


(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

(2022). An End-Effector for Robotic Cotton Harvesting. Smart Agricultural Technology 2, 100043.


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


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


(2022). Using Hyperspectral Imaging for Differentiating Cultivars, Growth Stages, Flowers and Leaves of Industrial Hemp. Frontiers in Plant Science 12, 810113.


(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.


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


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



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)


I am actively looking for a PhD graduate student to start in Fall 2023 / Spring 2024 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 Spring 2024 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 .