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 in January of 2023, I was a Postdoc research associate with USDA-ARS on the campus of MSU for 2018-2019, with research focused on optical imaging for quality evaluation of horticultural products and machine vision-based apple in-field sorting technology development (click here). Thereafter, I joined in the Department of Biological and Agricultural Engineering at North Carolina State University, starting the research on imaging-based high-throughput plant phenotyping and precision agriculture. I landed my first faculty position as an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University in 2020.

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).

Interests

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

Projects

Recent Publications

(2024). Automated handling and feeding techniques for skewering operations. Journal of the ASABE (under review).

(2024). OpenWeedGUI: An Open-Source Graphical Tool for Weed Imaging and YOLO-based Weed Detection. Computers and Electronics in Agriculture (under review).

Video

(2024). Prototyping and Evaluation of a Novel Machine Vision System for Real-time, Automated Quality Grading of Sweetpotatoes. Computers and Electronics in Agriculture (under review).

Video

(2024). Non-Destructive Assessment of Microbial Spoilage of Broiler Breast Meat Using Structured Illumination Reflectance Imaging with Machine Learning. Food Analytical Methods (in press).

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(2024). Beef marbling assessment by structured illumination reflectance imaging with deep learning. Journal of Food Engineering 369, 111936.

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(2023). Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based Approach. Computers and Electronics in Agriculture 216, 108517.

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(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 66(6): 1437-1447.

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

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

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

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

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

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

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

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(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

Teaching

2024 Spring: BE815 Experimentation and Instrumentation for Biosystems Engineering (3 credits, lecture/lab), Michigan State University

2023 Fall: BE491/891 Sensors & Robotics for Agricultural Systems (3 credits, lecture), Michigan State University

2022 Fall: ABE 4463/6463 Introduction to Imaging in Biological Systems (3 credits, lecture), Mississippi State University

2022 Spring: ABE 4423/6423 Bioinstrumentation II (3 credits, lecture/lab), Mississippi State University

2021 Fall: ABE 4990/6990 Introduction to Imaging in Biological Systems (3 credits, lecture), Mississippi State University

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

News

Openings

I am actively looking for PhD (and or MS) graduate students to start in Summer/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 quality assessment of horticultural produce and meat, in-orchard sensing & automation of specialty crops, imaging-based high-throughput phenotpyping, and artificial intelligence (AI) 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 Spring/Summer/Fall 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.

Contact