Yuzhen Lu
Assistant Professor
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Positions (2)
Yuzhen Lu
Michigan State University
Postdoc Position: Specialty Crop On-Farm Automation at Michigan State University . This postdoctoral research associate opening is in Dr. Yuzhen Lu’s group (AgFood Sensing & Intelligence) in the Department of Biosystems & Agricultural Engineering. The project focuses on on-farm automation of specialty crops , especially in-orchard/postharvest automation and AI-enabled mechanization for labor-intensive production tasks. The successful candidate will work on machine vision , artificial intelligence , automation technologies , robotics/mechatronics , and related engineering solutions for specialty crop production. Example tasks mentioned include mechanized harvesting, automated sorting, and vision-based crop management. The role also involves generating peer-reviewed publications, discussing design/prototyping/algorithm development/testing with Dr. Lu, and potentially contributing to grant proposals and teaching assistance. Eligibility highlights: a PhD in Biosystems/Agricultural Engineering, Electrical Engineering, Mechanical Engineering, Automation, or a closely related field; publication experience in machine/computer vision, mechatronics, or AI/robotics; strong programming skills in C++, Python, and/or Matlab; and excellent scientific writing and communication skills. Preferred experience includes engineering design, hardware prototyping, system integration, and real-time mechanical vision systems for special crop automation. Funding and duration: the appointment is initially for 12 months, with possible extension depending on performance and funding. No stipend amount is listed. Application: email [email protected] to apply or express interest. Review begins immediately and continues until the position is filled. A video pre-interview may be arranged for shortlisted candidates.
Yuzhen Lu
Michigan State University
Michigan State University is advertising a PhD/MS position in AI and 3D Vision for on-farm pig monitoring . The opportunity is associated with Assistant Professor Yuzhen Lu and appears to focus on research at the intersection of computer science , computer vision , and agriculture , with possible relevance to animal monitoring and precision farming. This is a student opening rather than a scholarship announcement. The post does not specify funding, stipend, tuition coverage, or a deadline, so applicants should check the linked post for the latest details. The position is explicitly described as available right now. Prospective applicants interested in AI , 3D vision , computer vision , and agricultural technology should review the linked announcement and contact the supervisor if needed. The post indicates both PhD and MS level openings.
Articles (16)
Year:
2024
Weed management impacts crop yield and quality. Machine vision technology is crucial to the realization of site-specific precision weeding for sustainable crop production. Progress has been made in developing computer vision algorithms, machine learning models, and datasets for weed recognition, but there has been a lack of open-source, publicly available software tools that link imaging hardware and offline trained models for system prototyping and evaluation, hindering community-wise development efforts. Graphical user interfaces (GUIs) are among such tools that can integrate hardware, data, and models to accelerate the deployment and adoption of machine vision-based weeding technology. This study introduces a novel GUI called OpenWeedGUI, designed for the ease of acquiring images and deploying YOLO (You Only Look Once) models for real-time weed detection, bridging the gap between machine vision and artificial intelligence (AI) technologies and users. The GUI was created in the framework of PyQt with the aid of open-source libraries for image collection, transformation, weed detection, and visualization. It consists of various functional modules for flexible user controls and a live display window for visualizing weed imagery and detection. Notably, it supports the deployment of a large suite of 31 different YOLO weed detection models, providing flexibility in model selection. Extensive indoor and field tests demonstrated the competencies of the developed software program. The OpenWeedGUI is expected to be a useful tool for promoting community efforts to advance precision weeding technology.
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2024
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2022
Collaborators (4)
Jiajun Xu
Michigan State University
Boyang Deng
Michigan State University
Ebenezer Olaniyi
Mississippi State University
Xin Zhang
Assistant Professor
Mississippi State University
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