July 2, 2025 – A team of researchers led by Dr. Ittai Herrmann at the Hebrew University of Jerusalem in collaboration with Virginia State University, University of Tokyo, and the Volcani Institute, has developed an advanced drone-based system that offers, for the first time, a smarter way to monitor crop stress and health.
Published in the ISPRS Journal of Photogrammetry and Remote Sensing, the study showcases how unmanned aerial vehicles (UAVs) equipped with hyperspectral, thermal, and RGB sensors, can work in tandem with artificial intelligence models to diagnose complex crop stress scenarios.
Traditional remote sensing methods often fall short of detecting combined environmental stresses such as water and nutrient shortages. The researchers have developed a powerful method for detecting simultaneous nitrogen and water deficiencies in field-grown sesame plants. This innovative approach leverages cutting-edge UAV imaging technology and artificial intelligence to improve the accuracy of stress detection in crops. The integration of multiple data sources enables the identification of combined nutrient and water-related deficiencies.
“By integrating data from multiple UAV-imaging sources and training deep learning models to analyze it, we can now distinguish between stress factors that were previously challenging to tell apart,” said Dr. Ittai Herrmann of the Robert H. Smith Faculty of Agriculture, Food & Environment, where the experiments were conducted. “This capability is vital for precision agriculture and for adapting to the challenges of climate change.”
The team of researchers applied an advanced drone-based system that accurately detects combined nitrogen and water deficiencies in field-grown sesame, paving the way for more efficient and sustainable farming. Sesame, a climate-resilient oilseed crop with growing global demand, was chosen due to its nutritional importance and potential for expansion into new agroecosystems.
The team’s multimodal ensemble approach improved classification accuracy of combined nutrient and water stress from just 40–55% using conventional methods to an impressive 65–90% with their custom-developed deep learning system.
This new remote-sensing method may enable growers to reduce fertilizer and water use while maintaining yield, improving both economic and environmental outcomes.
The research paper titled “Multimodal ensemble of UAV-borne hyperspectral, thermal, and RGB imagery to identify combined nitrogen and water deficiencies in field-grown sesame” is now available in ISPRS Journal of Photogrammetry and Remote Sensing and can be accessed here.
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