Skip to main content
ARS Home » Southeast Area » Mississippi State, Mississippi » Crop Science Research Laboratory » Genetics and Sustainable Agriculture Research » Research » Publications at this Location » Publication #423789
Research Project: Dynamic, Data-Driven, Sustainable, and Resilient Crop Production Systems for the U.S.

Location: Genetics and Sustainable Agriculture Research

Title: Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems

Author
item UPADHYAY, ARJUN - North Dakota State University
item G C, SUNIL - North Dakota State University
item MAHECHA, MARIA - North Dakota State University
item METTLER, JOSEPH - North Dakota State University
item HOWATT, KIRK - North Dakota State University
item ADERHOLDT, WILLIAM - Grand Farm Innovation Campus
item OSTLIE, MICHAEL - North Dakota State University
item SUN, XIN - North Dakota State University

Submitted to: Data in Brief
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/13/2025
Publication Date: 3/25/2025
Citation: Upadhyay, A., G C, S., Mahecha, M.V., Mettler, J., Howatt, K., Aderholdt, W., Ostlie, M., Sun, X. 2025. Weed-crop dataset in precision agriculture: Resource for AI-based robotic weed control systems. Data in Brief. 60(2025)111486:1-8. https://doi.org/10.1016/j.dib.2025.111486.
DOI: https://doi.org/10.1016/j.dib.2025.111486

Interpretive Summary: Weeds significantly impact crop production, and current control methods have drawbacks. To improve robotic weeding precision, researchers created a dataset of 1120 labeled images showcasing five weed and eight crop species in diverse field conditions. Captured with a camera mounted on a remote-controlled robot, these images mimic real-world variability, enabling deep learning models to better identify weeds for targeted removal. This resource aids in developing more accurate and efficient robotic weed control, contributing to sustainable agriculture.

Technical Abstract: Effective weed management is crucial for maintaining optimal crop growth and achieve higher yield. Recent advancement in robotic technologies and advanced deep learning (DL) models is shaping the future of robotic weed control systems. However, DL models for weed identification requires substantial amount of data collected in natural field conditions. This article presents red, green, and blue (RGB) datasets for multiple weed species found across different crop production systems. DL models require sophisticated datasets for training the model to achieve high object detection accuracy. To achieve this, a real field dataset was collected under diverse environmental conditions to mimic the natural environment and exhibits the variability in datasets. This aims to improve the accuracy of deep learning models for real time weed identification in precision agriculture. The dataset presented in this article was collected using Canon RGB camera, mounted on the front of remote-controlled robotic platform. This dataset comprises 1120 labelled images presenting five species of weeds and eight different crop species. This resource can be utilized by researchers, educators, and students in developing DL models for weed identification. The dataset can be further enriched by combining it with other relevant weed-crop datasets to create more diverse and robust datasets. This will enhance the capabilities of DL algorithms to be integrated with robotic weed control platforms for precision weed management.