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., machine vision, optical imaging, and spectroscopy) and automation/robotics technologies for addressing pressing challenges in agricultural systems encompassing production and postharvest processes.

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 (click here) 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.

Fo prospective students who are determined to work with me, you need to be devoted, proactive, creative, and adaptive, while being willing to go beyond your comfortable zone and embrace challenges. Research is never trivial but a real journey. I will strive my best to support you to fulfill your academic goals and enhance your profession and beyond. If you would like to have a glimpse of my personality, you may read some of my writings (in Chinese).


  • Optical Sensing
  • Machine Vision
  • Food Inspection
  • Precision Agriculture
  • Plant/Animal 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


Recent Publications

(2023). Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach. Computers and Electronics in Agriculture (in press).


(2023). Field Test and Evaluation of an Innovative Vision-Guided Robotic Cotton Harvester. Computers and Electronics in Agriculture (in press).

(2023). Fusing Spectral and Spatial Features of Hyperspectral Reflectance Imagery to Differentiate between Normal and Defective Blueberries. Smart Agricultural Technology (under review).

(2023). Non-destructive Assessment of White Striping in Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Deep Learning. Journal of the ASABE (accepted).

(2023). Online Volume Measurement of Sweetpotatoes by A LiDAR-based Machine Vision System. Journal of Food Engineering 361, 111725.


(2023). Development of a singulation system for handling catfish fillets. Int. J. Adv. Manuf. Technol. 5(2), 941-949.


(2023). Optimal sampling-based path planning for mobile cable-driven parallel robots in highly constrained environment. Complex & Intelligent Systems.


(2023). Integration and Preliminary Evaluation of A Robotic Cotton Harvester Prototype. Computers and Electronics in Agriculture (211), 107943.


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


(2023). Efects of fine 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). 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


2023 Fall: BE491/891 Sensors & Robotics for Agricultural Systems, Michigan State University

2022 Fall: ABE 4463/6463 Introduction to Imaging in Biological Systems, Mississippi State University

2022 Spring: ABE 4423/6423 Bioinstrumentation II, Mississippi State University

2021 Fall: ABE 4990/6990 Introduction to Imaging in Biological Systems, Mississippi State University

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 Spring/Fall 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 postharvest sensing and assessment of food quality and safety, in-orchard sensing & automation, imaging-based plant phenotpyping, and artificial intelligence and robotics for precision plant/animal production. Students with engineering backgrounds (e.g., agricultural/food engineering, mechanical engineering, electrical engineering, computer science, etc.) and strong experience in computer vision and machine learning are highly welcome to apply. Please email me (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 Postdoc positions focusing on real-time machine vision system development for in-orchard and postharvest applications. The position can be started in Fall/Spring 2024 (for an intial one year with possibility of extension). Qualifications for this position include demonstrated experience in machine/computer vision, sensors and control, software-hardware integration, and strong publication records in engineering journals.

See the Postdoc position.