ADA 2025: A Multimodal Dataset for Water-Deficit Stress Detection in Sugar Beets
Authors: Piper Powell, Robert Franz, Amos Smith, Isaak Ihorst, Joachim Hertzberg, Martin Atzmueller
Organizations: Institute of Computer Science, Osnabrück University, Osnabrück, Germany · German Research Center for Artificial Intelligence (DFKI), Osnabrück, Germany
Research and development into artificial intelligence (AI) and machine learning (ML) applications in crop management are hindered by a lack of suitable public datasets. Whereas applications in disease and pest detection benefit from a comparatively higher amount of available data, applications like water-deficit stress detection often have very few if any public datasets available. Moreover, most datasets that are available are single-modality and frequently RGB-only, leaving the early detection and enhanced performance offered by other methods, and particularly by multimodal approaches, out of reach. Datasets with accurate metadata, higher numbers of plants, and smaller intervals between data points are also scarce. To address this, we publicly release ADA 2025, a >500 GB comprehensive multimodal dataset of young sugar beet plants under healthy and water-deficit conditions combining thermal, hyperspectral, RGB, and depth imagery along with relevant environmental and metadata and both relative water content (RWC) and leaf thickness as ground truth sources. Exemplary results showcase the dataset's validity in terms of differences detectable between stressed and healthy plants.
Figures & tables
Fig. 2: Overview of plant grid in one bed. Leaf sensors can be seen prepared at certain positions, awaiting attachment once the plants achieve the target growth stage.
Fig. 3: The camera package of the ADA robot, with the Pika L hyperspectral camera on the left, the A70 thermal camera on the right, and the Pico Flexx depth camera and Logitech webcam in the center, proximal and distal to the viewer respectively.
Modality
Measurement
Rate
Total
Camera Data
RGB
Color
15 img./plant/run
761,400
Depth
Depth, Intensity
15 img./plant/run
694,440
Thermal
Infrared
5 img./plant/run
231,373
Hyperspectral
Spectral
1 img./plant/run
50,544
Sensor Data
TABLE I: ADA 2025 Dataset Contents by Modality
Fig. 4: Trajectories of plant height, NDVI, SIPI, and leaf-air temperature over time in the control and treatment bed. The irrigation cutoff on September 3 is marked for reference. Shaded bands indicate ±1 standard deviation. Note that the 3pm thermal capture of the treatment bed failed on September 11; the reading shown for that day comes from a re-run at 4pm.
Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar 140001, India · Department of Robotics and Autonomous Systems, Plaksha University, Mohali 140306, India
Department of Information Technology, National Institute of Technology, Hazratbal Srinagar 190006, Jammu & Kashmir, India · Department of Computer Science and Engineering, Indian Institute of Technology, Ropar Rupnagar 140001, Punjab, India