Andi Isti Sakinah
Papers
1
Total Citations
5
H-Index
1
About
Andi Isti Sakinah is a researcher advancing the field of agricultural phenotyping, with a focus on integrating computational methods to improve crop breeding. Her key research areas include image-based phenotyping, multivariate statistical analysis, and the non-destructive estimation of yield-related traits in horticultural crops. Sakinah’s major contribution lies in developing a novel approach that combines high-throughput image analysis with multivariate models to accurately estimate fruit fresh weight in lowland tomatoes, as demonstrated in her highly cited 2024 paper. This work addresses a critical bottleneck in conventional breeding—the need for destructive, time-consuming measurements—by offering a rapid, non-invasive alternative that can accelerate selection in segregation lines. With 5 citations already, her research is gaining traction among plant scientists and breeders seeking efficient phenotyping tools. Sakinah’s achievements highlight her role in bridging computer vision and plant science, providing practical solutions for improving marketable yields in challenging lowland environments. Her work promises to empower breeders and farmers with data-driven methods for more sustainable and productive agriculture.
Research Focus
Key Achievements
Top Papers
- 1