Muhammad Fuad Anshori
Papers
1
Total Citations
5
H-Index
1
About
Muhammad Fuad Anshori is a rising agricultural scientist whose work bridges precision phenotyping and crop improvement, with a focus on lowland tomato breeding. His research centers on integrating image-based phenotyping (IBP) with multivariate statistical methods to non-destructively estimate key agronomic traits, such as fruit fresh weight—a critical parameter for marketable yield. His most-cited paper, "Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes" (2024), has already garnered 5 citations, signaling early impact in the field. This work addresses a longstanding challenge in breeding: the need for rapid, non-invasive measurements that can replace destructive sampling, thereby accelerating selection cycles. By demonstrating how IBP coupled with multivariate models can accurately predict fruit weight in segregating populations, Anshori provides breeders and farmers with a practical tool to enhance productivity. His contributions are particularly relevant for tropical agriculture, where lowland tomato cultivation faces heat and stress pressures. As an emerging voice in digital phenomics, Anshori’s research holds promise for scalable, data-driven crop improvement strategies.
Research Focus
Key Achievements
Top Papers
- 1