Marlina Mustafa
Papers
1
Total Citations
5
H-Index
1
About
Marlina Mustafa is a researcher at the forefront of applying image-based phenotyping and multivariate analysis to agricultural science, with a particular focus on improving crop yield estimation. Her most cited work, "Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes" (2024), addresses a critical challenge in breeding: the need for non-destructive, high-throughput methods to assess fruit weight. By integrating advanced imaging techniques with statistical modeling, Mustafa’s research enables breeders and farmers to predict marketable yields without physically harvesting or damaging fruit—a significant advancement for precision agriculture. This work has already garnered 5 citations, reflecting its timely relevance in a field increasingly driven by data and automation. Mustafa’s contributions lie at the intersection of plant phenomics, computational analysis, and practical breeding, offering scalable solutions for lowland tomato production. Her approach not only streamlines selection processes for desirable traits but also reduces labor and resource waste, making her a key figure in the push toward more efficient, technology-driven crop improvement.
Research Focus
Key Achievements
Top Papers
- 1