Precision agriculture leverages advanced technologies to optimize crop management, increase yield and promote sustainable farming practices. Despite significant progress in agricultural automation, continuous field monitoring remains a challenge for farmers due to labor demands and variable environmental conditions. To address this, the use of mobile robots equipped with intelligent perception systems enables autonomous data collection and analysis in real agricultural environments. This work presents a dataset focused on crop monitoring, containing images of corn and beet fields captured by a ground mobile robot. The images were acquired using the Summit XL platform from Robotnik, equipped with an Intel RealSense D455 camera and collected under natural daylight conditions. The robot was teleoperated across the crop fields while recording rosbags that include RGB images, suitable for tasks such as plant detection. The dataset comprises 10080 images organized following the YOLO object detection format, with 9104 training images, 493 validation images, and 483 test images. All images are annotated with bounding boxes in normalized YOLO format, distinguishing between two crop classes: beet and corn. To enhance model robustness, the dataset includes augmented versions created through geometric transformations and photometric variations. Privacy protection measures were implemented using automated person detection and anonymization. This dataset aims to support research in precision agriculture, particularly in developing intelligent systems for crop monitoring, plant health assessment, and autonomous agricultural inspection. All data are publicly available through a single Hugging Face repository.
Fuente et al. (2026) studied this question.