Yigal Elad

Papers

Total Citations

198

H-Index

About

Yigal Elad is a pioneering researcher at the forefront of precision agriculture and plant pathology, specializing in robotic disease detection for greenhouse crops. His major contributions lie in developing autonomous systems that integrate computer vision and machine learning to identify and monitor plant diseases in real time, with a primary focus on bell pepper cultivation. Elad’s landmark 2016 paper, "Robotic Disease Detection in Greenhouses: Combined Detection of Powdery Mildew and Tomato Spotted Wilt Virus," has garnered 143 citations, establishing a foundational framework for combining detection of multiple pathogens—powdery mildew and Tomato spotted wilt virus (TSWV)—using a single robotic platform. This work addresses critical challenges in greenhouse management, aiming to improve disease control, boost yield, and reduce pesticide use through early, precise intervention. His subsequent 2017 study (48 citations) and earlier 2015 work (7 citations) further advanced robotic monitoring systems, tackling real-world obstacles like variable lighting and plant occlusion. Elad’s research has significant implications for sustainable agriculture, offering scalable solutions that empower growers with data-driven tools. His achievements underscore a commitment to bridging robotics and phytopathology, making him a key figure in the evolution of smart farming technologies.