Gefen Kohavi
Papers
1
Total Citations
2
H-Index
1
About
Gefen Kohavi’s research lies at the intersection of agricultural technology, computer vision, and plant phenotyping, with a focus on automating the measurement of complex plant traits. In their most cited work, "Top Down Approach to Height Histogram Estimation of Biomass Sorghum in the Field" (2018), Kohavi addresses a critical bottleneck in modern genetics: the slow, manual collection of phenotype data. By developing an algorithm that automatically estimates canopy height from overhead imagery, Kohavi enables high-throughput, non-destructive analysis of sorghum—a key bioenergy crop. This contribution is particularly valuable for bridging the gap between rapid genotyping advances and the lagging phenotyping pipeline, allowing researchers to more efficiently map genotype to phenotype. While their citation count (2) reflects a focused, emerging impact, the work demonstrates a practical, scalable solution to a pressing problem in precision agriculture. Kohavi’s approach exemplifies how computational methods can accelerate field-based research, making them a promising voice in the drive toward automated, data-driven crop improvement.
Research Focus
Key Achievements
Top Papers
- 1