Crop Yield Prediction
Improving crop productivity while maintaining environmental sustainability represents one of the greatest challenges facing agriculture. DALab develops advanced predictive frameworks that combine remote sensing, weather information, soil properties, and machine learning to better understand crop growth and yield variability.
Our research investigates how multimodal datasets can be integrated to improve the prediction of crop performance under varying environmental conditions. By combining agronomic knowledge with artificial intelligence, we seek to provide producers and stakeholders with reliable tools that support precision management and reduce production risks.
Hyperspectral reflectance data acquisition under field and laboratory conditions. (A) canopy-level field measurements conducted under natural illumination. (B) laboratory spectroscopy setup for leaf sample measurements under controlled illumination. https://ars.els-cdn.com/content/image/1-s2.0-S2772375526000250-gr1.jpg
Hyperspectral reflectance data acquisition under field and laboratory conditions. (A) canopy-level field measurements conducted under natural illumination. (B) laboratory spectroscopy setup for leaf sample measurements under controlled illumination. https://ars.els-cdn.com/content/image/1-s2.0-S2772375526000250-gr1.jpg
These technologies have the potential to enhance resource efficiency, improve profitability, and contribute to more resilient agricultural systems capable of adapting to future climate challenges.
Team Members/ Collaborators
Publications
Joint prediction of potato yield and nitrogen status at row closure using multi-task deep learning and UAV-derived LiDAR–multispectral data Ehsan Chatraei Azizabadi, Nasem Badreldin, Computers and Electronics in Agriculture, Volume 250, 2026, 111893, https://doi.org/10.1016/j.compag.2026.111893
VNIR-SWIR hyperspectral spectroscopy for nitrogen assessment in potato crops: Deep learning and regression models across field and laboratory conditions Ehsan Chatraei Azizabadi, Masoomeh Gomroki, Mohamed El-Shetehy, Keshav D. Singh, Abdul-Wahab Mossa, Nasem Badreldin, Smart Agricultural Technology, Volume 13, 2026, 101801, https://doi.org/10.1016/j.atech.2026.101801
CWRepViT-Net: An encoder-decoder deep learning framework with RepViT blocks for crop weed semantic segmentation in soybean fields through their life journey Masoomeh Gomroki, Dilshan Benaragama, Christopher James Henry, Nasem Badreldin, Robert Gulden, Smart Agricultural Technology, Volume 12, 2025, 101472, https://doi.org/10.1016/j.atech.2025.101472
A Review on Potato Crop Yield and Nitrogen Management Utilizing Remote/Proximal Sensing Technologies and Machine Learning Models in Canada Azizabadi, E.C., Badreldin, N., Potato Res. 68, 1659–1679 (2025). https://doi.org/10.1007/s11540-024-09803-3
In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning Chatraei Azizabadi, E., El-Shetehy, M., Cheng, X., Youssef, A., & Badreldin, N. (2025). Remote Sensing, 17(11), 1860. https://doi.org/10.3390/rs17111860