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A team of researchers led by Dr. Ittai Herrmann at The Hebrew University of Jerusalem, in collaboration with Virginia State University, the University of Tokyo and the Volcani Institute, has 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.

Published in the ISPRS Journal of Photogrammetry and Remote Sensing, the 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 in detecting combined environmental stresses such as water and nutrient shortages. This study is among the first to successfully address this challenge in an indeterminate crop such as sesame.

"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. Herrmann. "This capability is vital for precision agriculture and for adapting to the challenges of climate change."

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.

The field experiment was conducted at the Experimental Farm of Robert H. Smith Faculty of Agriculture in Rehovot. Seeds were supplied by Prof. Zvi Peleg. Rom Tarshish, an MSc student at the time, grew sesame plants under varied irrigation and nitrogen treatments and acquired plant traits and leaf level spectral data.

Dr. Maitreya Mohan Sahoo analyzed the UAV-imagery through machine learning pipelines to generate maps of leaf nitrogen content, water content, and other physiological traits, which helped identify early stress markers.

Sesame, a climate-resilient oilseed crop with growing global demand, was chosen due to its nutritional importance and potential for expansion into new agro-ecosystems. This new remote-sensing method may enable growers to reduce fertilizer and water use while maintaining yield, improving both economic and environmental outcomes.