About

Dr. John F. Reid is a Research Professor in Computer Science and Agricultural & Biological Engineering at the University of Illinois Urbana-Champaign, where he also serves as the Executive Director of the Center for Digital Agriculture. He has 40 years of experience in technology leadership within industry and academia, with a research focus on advancing agricultural and biological systems through sensing, automation, and control. His academic career at the University of Illinois began in 1986, with his research initially centered on sensing, automation, and control of food and agricultural systems. Reid has held various professional roles, including Vice President of Enterprise Technologies at Brunswick Corporation and Director of Product Technology & Innovation at John Deere, where he was recognized as a John Deere Technical Fellow for his contributions to technology innovation in 2017. His research interests encompass the translation of research into practice, outcome-driven innovation processes, business model innovation, and innovation management, with particular emphasis on circular bioeconomy in agriculture, precision agriculture technologies, agricultural robotics, and automation. Reid has contributed significantly to the field with over 30 patents and has been recognized with numerous awards, including election to the National Academy of Engineering in 2019 for his work in automation in agriculture.

Research topics

• Computer Science
• Engineering
• Electronic engineering
• Optics
• Physics
• Telecommunications
• Agricultural economics
• Business
• Waste management
• Biology
• Economics
• Ecology
• Natural resource economics

Selected publications

• Evaluation of YOLO-based weed detection models on commercial horseradish fields in Southern Illinois

  Frontiers in Agronomy · 2026-04-14

  articleOpen accessSenior author

  Horseradish ( Armoracia rusticana ) is a high-value specialty crop whose production in Southern Illinois is constrained by limited herbicide options and labor-intensive weed management, particularly in commercial fields. This study developed a multisource image dataset and evaluated lightweight deep learning models to enable real-time, vision-based weed detection for future robotic weeding systems in commercial horseradish fields of Southern Illinois. Image data collection was conducted during the 2024 growing season at two commercial fields and one research site using a handheld smartphone and an unmanned ground vehicle (Farm-ng Amiga robotic platform) equipped with two stereo cameras (Luxonis OAK-D cameras). The data was first annotated, assigning appropriate labels to horseradish and weed instances, then augmented to improve data diversity. We trained and compared nine YOLO models (v8, v11, v12; nano, small, medium) using standard object detection metrics (precision, recall, F1-score, mAP@50) and computational indicators (inference time, GFLOPs, model size, training time), and selected the best configuration for hyperparameter tuning with an automated search over learning rate, regularization, and optimizer. The tuned YOLOv8-nano model achieved the best balance of detection performance and computational efficiency, and was subsequently benchmarked on multiple desktop and edge-computing platforms to assess real-time feasibility. The results demonstrated that lightweight YOLO architectures can provide accurate, fast horseradish-weed detection suitable for deployment on embedded hardware, offering a key sensing component for future autonomous mechanical weeding in commercial horseradish production. This study makes three key contributions: (i) a multisource image dataset of horseradish and weeds collected from both commercial and research fields using manual imaging and a robotic platform; (ii) an evaluation protocol that combines accuracy metrics with computational indicators to guide model selection for embedded deployment; and (iii) a cross-platform benchmarking workflow that assesses the real-time feasibility of lightweight YOLO models on desktop and edge-computing hardware for robotic weeding applications.

  PublisherDOI

• Eye-Safe Terabit-Class WDM Optical Wireless: How Many Channels are Enough?

  2024-01-01 · 1 citations

  articleCorresponding

  On the path towards Terabit-class optical wireless, the use of WDM technology poses many practical questions. Supported by a 1.8 km field-trial, and multiplexing up to 16×200G channels, we expose the tradeoffs between capacity and reliability depending on the channel count, optical pre-amplification architecture and coding requirements.

  PublisherDOI

• Identification of Advantages and Limitations of Current Risk Assessment and Hazard Analysis Methods when Applied on Autonomous Agricultural Machineries

  Journal of Agricultural Safety and Health · 2024-01-01 · 1 citations

  articleOpen access

  HIGHLIGHTS: The three main types of risk assessment and hazard analysis techniques applied on autonomous agricultural machines are: (1) Informal Group Analysis; (2) Hazard Analysis and Risk Assessment (HARA); and (3) Failure Mode and Effects Analysis (FMEA). Replicability is the main advantage of FMEA and HARA, while cost effectiveness is the main advantage of Informal Group Analysis. Subjectivity and the requirement for prior knowledge (data) are the main weaknesses of FMEA, HARA, and Informal Group Analysis when applied to novel and revolutionary autonomous agricultural machines. ABSTRACT: In the last ten years, the development of automated agricultural machinery has seen noteworthy advancements. Nevertheless, the successful commercialization of these technologies depends critically on their ability to operate safely. This study evaluated the advantages and limitations of current risk assessment and hazard analysis methods currently used to ensure the safety of autonomous agricultural machines. An online survey containing 18 questions was distributed to 711 participants identified as potential individuals who are currently working or have worked on autonomous agricultural machines to determine the type and frequency of risk assessment and hazard analysis methods applied on autonomous agricultural machines, examine the advantages and limitations of each method, and investigate the perceived effectiveness of each method. Frequency analysis was used to determine the most and least utilized risk assessment and hazard analysis methods. The advantages and limitations of each risk assessment and hazard analysis approach were compared. Descriptive statistics (counts, means, medians, percent) and frequency analysis of the variables were used. The three main types of risk assessment and hazard analysis techniques applied to autonomous agricultural machines. The methods are (a) Informal Group Analysis (e.g., Brainstorming), (b) Hazard Analysis and Risk Assessment (HARA), and (c) Failure Mode and Effects Analysis (FMEA). Replicability is perceived as the main advantage of FMEA and HARA, while cost-effectiveness is the main advantage of Informal Group Analysis. The need to have pre-existing data of the autonomous agricultural machine at hand to be able to perform risk assessment and subjectivity are the main limitations of FMEA, HARA, and Informal Group Analysis dealing with novel and revolutionary autonomous agricultural machines. Industry experts do not believe that the risk assessment and hazard analysis procedures now used are reliable and efficient enough to guarantee the safety of autonomous agricultural tractors. This study reveals important information about the current state of risk assessment and hazard analysis methods in the context of autonomous agricultural machinery. This knowledge can inform future research, policy development, and industry practices to ensure the safety of autonomous agricultural machines.

  PublisherOA PDFDOI

• 100G FSO field trial with transmitter power adaptability using a LoRa feedback channel

  Journal of Optical Communications and Networking · 2024-01-02 · 7 citations

  articleCorresponding

  The popularity of free-space optics (FSO) communications is rising as a key enabler for widespread communications since it can provide fiber-like connectivity to regions where fiber deployment is not feasible and radio-frequency (RF) technologies cannot comply with the bandwidth requirements. However, the reliability of FSO links is severely affected by fluctuations in the received optical power caused by weather instability, atmospheric turbulence, and pointing errors. In this work, we propose an adaptive power pre-compensation system enabled by a low-cost, long-range (LoRa) RF feedback link. We experimentally demonstrate this system in a 1.8 km field trial supporting 100 Gbps coherent FSO transmission under approximately 10 dB of slow-fading over a 16 h period. Our results show a considerable reduction of up to 5 dB in the average dynamic range of the receiver and substantial improvement in the link reliability (7% on average) when compared with an estimated fixed power transmission. Further reliability or data-rate improvements can also be achieved with joint optimization of the FEC overhead and the adaptive power transmission scheme.

  PublisherDOI

• An Index for Quantifying Circularity of Bioeconomy Systems

  SSRN Electronic Journal · 2024-01-01

  preprintOpen accessSenior author
  PublisherDOI

• A scalable index for quantifying circularity of bioeconomy systems

  Resources Conservation and Recycling · 2024-08-01 · 12 citations

  articleSenior author
  PublisherDOI

• 4 Tbps+ FSO Field Trial Over 1.8 km With Turbulence Mitigation and FEC Optimization

  Journal of Lightwave Technology · 2024 · 29 citations

  • Computer Science
  • Computer Science
  • Electronic engineering

  The future of wireless communication requires the unique capabilities of free-space optics (FSO). Therefore, it is crucial to develop methods for achieving reliable long-distance FSO communications. This study presents a field trial of a 1.8 km FSO link that can achieve 4 Tbps+ using coherent optics, wavelength division multiplexing (WDM), atmospheric turbulence mitigation, and optimized forward error correction (FEC) coding. Our study focuses on the impact of atmospheric turbulence on FSO communication and the methods used to mitigate its effects. We compensate for the turbulence-induced power fluctuations by using an optical pre-amplification technique with automatic power control (APC) to stabilize the received power. This technique reduces the perceived Rytov variance by a factor of ten, making the FSO communication more reliable and efficient. Furthermore, we explore the maximization of net bit-rate by optimising the transmitted channels' FEC overhead, considering two different architectures: individual and joint wavelength processing. The proposed optimization techniques are shown to provide significant gains both in terms of capacity and reliability, making FSO technology a more practical solution for long-range wireless communication.

  PublisherOA PDFDOI

• Autonomous navigation and path planning for agricultural robots

  Burleigh Dodds series in agricultural science · 2024-03-26

  book-chapter1st authorCorresponding

  Navigation and path planning are essential technologies for increasing the productivity of agriculture machine systems performing modern precision agriculture tasks. Production agriculture requires efficient methods for complete coverage of agricultural landscapes to complete the critical production steps of preparing the land and planting, managing, and harvesting crops. To help farmers to make the transformation from automated to autonomous systems requires approaches that can leverage the current automation advances from modern precision agricultural machinery and build on them as tools in the development and deployment of agricultural robots. This chapter provides a high-level overview of critical elements in autonomous navigation and path planning and discusses the opportunities and challenges related to building on precision agriculture technologies to enable productive agricultural robots.

  PublisherDOI

• Achieving multi-terabit FSO capacity with coherent WDM transmission over a 1.8 km field trial

  IET conference proceedings. · 2023 · 7 citations

  • Computer Science
  • Computer Science
  • Optics

  Using terrestrial optical head prototypes, we experimentally demonstrate 4 Tbps transmission over a field-deployed 1.8 km FSO-link resorting to coherent optics, wavelength multiplexing, optimized coding, and atmospheric turbulence mitigation through optical pre-amplification.

  PublisherDOI

• Unraveling “Fiber in the Sky”: Terabit Capacity Enabled by Coherent Optical Wireless

  IEEE Communications Magazine · 2023-10-23 · 12 citations

  articleOpen access

  The quest for high-speed optical wireless communications is now entering a new and ambitious phase of pursuing fiber-like capacity over the air. However, legacy optical wireless technologies relying on simple intensity modulation and direct detection will not be up to the challenge of providing Terabit data-rates for wireless communications. Benefitting from the latest developments on coherent optical fiber transceivers, unprecedented optical wireless capacity can now be achieved, effectively enabling the wireless delivery of Terabit-range data-rates. In this work, we exploit advanced features provided by state-of-the-art coherent transceivers, such as symbol-rate and bit-rate adaptability, to enhance the trade-off between capacity and reliability in optical wireless systems impaired by atmospheric turbulence, pointing errors, and Doppler shift. Targeting the digital mitigation of these challenges, a set of different field trials is provided, demonstrating the viability of per-channel bit-rates in the range of 400 Gb/s to 1 Tb/s, applicable to both terrestrial and SatCom scenarios.

  PublisherOA PDFDOI

Frequent coauthors

• Noboru Noguchi

  Hokkaido University

  19 shared

• Francisco Rovira-Más

  15 shared

• Qin Zhang

  Washington State University

  13 shared

• Marvin R Paulsen

  11 shared

• J. B. Litchfield

  11 shared

• Qin Zhang

  Institute of Physics

  10 shared

• Fernando P. Guiomar

  Instituto de Telecomunicações

  9 shared

• E.R. Benson

  University of Illinois Urbana-Champaign

  9 shared

Education

• Doctor of Philosophy, Agricultural Engineering

  Texas A&M University

  1987

• Master of Science, Agricultural Engineering

  Virginia Tech

  1982

• Bachelor of Science, Agricultural Engineering

  Virginia Tech

  1980

Awards & honors

• University of Illinois University Scholar (1995)
• ASABE Fellow (2004)
• John Deere Fellow (2017)
• Virginia Tech Academy of Engineering Excellence (2020)
• Elected to the National Academy of Engineering (2019)