People
Principal Investigator
Sunoj Shajahan
Assistant Professor
Sunoj was born and raised in Tamil Nadu, India. His research interests are in precision agriculture, remote sensing, and geospatial data analysis. During his PhD, he developed a few open-source tools using ImageJ to process and analyze field imagery, which also was the starting point in FOSS (Free and Open Source Software). As a postdoctoral associate at Cornell University, he worked on integrating remote sensing and data analysis to predict crop yield at scale. In Fall 2023, he joined University of Illinois Urbana-Champaign, where he started the Digital Precision Agriculture Lab. His lab focuses on advancing precision technologies and building data-driven, geospatial solutions that support practical decision-making in agriculture.
sunoj@illinois.edu | |
Assistant Professor
Sunoj was born and raised in Tamil Nadu, India. His research interests are in precision agriculture, remote sensing, and geospatial data analysis. During his PhD, he developed a few open-source tools using ImageJ to process and analyze field imagery, which also was the starting point in FOSS (Free and Open Source Software). As a postdoctoral associate at Cornell University, he worked on integrating remote sensing and data analysis to predict crop yield at scale. In Fall 2023, he joined University of Illinois Urbana-Champaign, where he started the Digital Precision Agriculture Lab. His lab focuses on advancing precision technologies and building data-driven, geospatial solutions that support practical decision-making in agriculture.
sunoj@illinois.edu | |
Graduate students
Sandesh Poudel
Ph.D. student
Sandesh is from Chitwan, Nepal and joined our team in Spring 2025. He earned his B.E. in Agricultural Engineering from Tribhuvan University, and his M.S. from the University of Georgia, where he developed the innovative Digital Agricultural Technology Readiness Level (DATRL) framework to assess agricultural technology maturity. His research interest includes robotics, artificial intelligence, remote sensing, and precision agriculture in order to develop sustainable farming solutions. Sandesh is currently working on developing solutions for automatic weed detection and removal using robotic platform. He is also the author of book, "Agricultural Extension Education" and has received multiple awards for his academic and leadership contributions.
spoudel2@illinois.edu | |
Ph.D. student
Sandesh is from Chitwan, Nepal and joined our team in Spring 2025. He earned his B.E. in Agricultural Engineering from Tribhuvan University, and his M.S. from the University of Georgia, where he developed the innovative Digital Agricultural Technology Readiness Level (DATRL) framework to assess agricultural technology maturity. His research interest includes robotics, artificial intelligence, remote sensing, and precision agriculture in order to develop sustainable farming solutions. Sandesh is currently working on developing solutions for automatic weed detection and removal using robotic platform. He is also the author of book, "Agricultural Extension Education" and has received multiple awards for his academic and leadership contributions.
spoudel2@illinois.edu | |
Xiaoyu Zhang
MS student, ABE
Zhang is from China. His research interests are in satellite image analysis, geospatial analysis, and machine learning. His MS thesis focuses on “Generalizing yield prediction and identifying key factors to improve the accuracy of prediction.”
xiaoyu26@illinois.edu
MS student, ABE
Zhang is from China. His research interests are in satellite image analysis, geospatial analysis, and machine learning. His MS thesis focuses on “Generalizing yield prediction and identifying key factors to improve the accuracy of prediction.”
xiaoyu26@illinois.edu
Seth Van Hoveln
Senior, Engineering Technology Management in Agricultural Sciences
Seth is from Milford, IL. He comes from a farm background and an expert in precision agriculture. He will graduate in Spring 2025, and is intersted in pursuing a MS and joining our team as a graduate student in Fall 2025.
sethtv2@illinois.edu
Senior, Engineering Technology Management in Agricultural Sciences
Seth is from Milford, IL. He comes from a farm background and an expert in precision agriculture. He will graduate in Spring 2025, and is intersted in pursuing a MS and joining our team as a graduate student in Fall 2025.
sethtv2@illinois.edu
Undergraduate students
Tanish Inamdar
Junior, Computer Science + Anthropology
Tanish is from Chandler, Arizona. Tanish Inamdar is developing a self-supervised image annotation pipeline using state-of-the-art foundation models to eliminate the manual annotation bottleneck in agricultural computer vision. His approach distills knowledge from a foundation model into a lightweight, field-deployable edge model for autonomous weed detection in horseradish fields. This annotation-free framework aims to be scalable and transferable to other specialty crop systems.
tanish2@illinois.edu
Junior, Computer Science + Anthropology
Tanish is from Chandler, Arizona. Tanish Inamdar is developing a self-supervised image annotation pipeline using state-of-the-art foundation models to eliminate the manual annotation bottleneck in agricultural computer vision. His approach distills knowledge from a foundation model into a lightweight, field-deployable edge model for autonomous weed detection in horseradish fields. This annotation-free framework aims to be scalable and transferable to other specialty crop systems.
tanish2@illinois.edu
Alif Harist
Senior, Engineering Technology Management in Agricultural Sciences
Alif is from Jakarta, Indonesia. He is developing a low-cost, modular precision sprayer for inter-row weeding in small- and medium-scale specialty crop farms, using horseradish as a case study. The $750 system integrates real-time computer vision with a targeted spray mechanism, achieving up to 97.6% spraying accuracy at practical field speeds. Its modular design allows the same platform to be adapted across other row crops, including pumpkin, without major redesign..
aharist2@illinois.edu |
Senior, Engineering Technology Management in Agricultural Sciences
Alif is from Jakarta, Indonesia. He is developing a low-cost, modular precision sprayer for inter-row weeding in small- and medium-scale specialty crop farms, using horseradish as a case study. The $750 system integrates real-time computer vision with a targeted spray mechanism, achieving up to 97.6% spraying accuracy at practical field speeds. Its modular design allows the same platform to be adapted across other row crops, including pumpkin, without major redesign..
aharist2@illinois.edu |
Vicky Liu
Junior, Agricultural and Biological Engineering. She is developing a non-AI method for detecting weeds in horseradish fields using texture-based image features. Her approach extracts texture metrics around each plant and applies a cutoff method to distinguish crops from weeds, achieving a mean error rate of 8.3% and a false negative rate of 3.6% across 402 images. By avoiding computationally expensive models, the method lowers hardware requirements and makes weed detection technology more accessible to growers.
vicky4@illinois.edu
Junior, Agricultural and Biological Engineering. She is developing a non-AI method for detecting weeds in horseradish fields using texture-based image features. Her approach extracts texture metrics around each plant and applies a cutoff method to distinguish crops from weeds, achieving a mean error rate of 8.3% and a false negative rate of 3.6% across 402 images. By avoiding computationally expensive models, the method lowers hardware requirements and makes weed detection technology more accessible to growers.
vicky4@illinois.edu
Hriday Sanakal
Hriday Sanakal is examining how crop yield prediction accuracy shifts across spatial scales and remote sensing platforms. He compared NDVI from Landsat 8, Sentinel-2, PlanetScope, and UAS imagery against measured yield at both county and field scales, developing transformation methods to map NDVI values between datasets. His results show a strong, tight linear relationship between Landsat NDVI and yield at the county scale, while field-scale predictions were more variable and less predictive across all platforms, highlighting the need for sensor harmonization in yield prediction models.
sanakal2@illinois.edu
Hriday Sanakal is examining how crop yield prediction accuracy shifts across spatial scales and remote sensing platforms. He compared NDVI from Landsat 8, Sentinel-2, PlanetScope, and UAS imagery against measured yield at both county and field scales, developing transformation methods to map NDVI values between datasets. His results show a strong, tight linear relationship between Landsat NDVI and yield at the county scale, while field-scale predictions were more variable and less predictive across all platforms, highlighting the need for sensor harmonization in yield prediction models.
sanakal2@illinois.edu