Plant Health Detection
Early identification of crop stress is critical for improving productivity and reducing environmental impacts. DALab develops innovative technologies that combine hyperspectral imaging, remote sensing, machine learning, and artificial intelligence to detect subtle physiological changes before visible symptoms appear.
Our research focuses on identifying diseases, nutrient deficiencies, and environmental stressors at their earliest stages, enabling more timely and effective management interventions. These technologies can help reduce unnecessary chemical inputs, improve resource use efficiency, and enhance crop resilience.
By shifting agriculture from reactive management toward predictive and proactive approaches, our work contributes to more sustainable and environmentally responsible crop production systems.
Team Members/ Collaborators
Publications
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
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
An Overview of Software Sensor Applications in Biosystem Monitoring and Control Badreldin, N.; Cheng, X.; Youssef, A. (2024). Sensors, 24, 6738. https://doi.org/10.3390/s24206738
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