Andi Dirpan
Papers
1
Total Citations
5
H-Index
1
About
Andi Dirpan is a researcher whose work sits at the intersection of agricultural science, phenotyping, and data-driven crop improvement. His primary research focus is on developing non-destructive, image-based methods to estimate key fruit traits, with a particular emphasis on tomatoes. Dirpan’s major contribution lies in demonstrating how combining image-based phenotyping (IBP) with multivariate statistical analysis can accurately predict fruit fresh weight—a critical parameter for breeders and farmers aiming to boost marketable yields. This approach offers a significant advantage over conventional, destructive measurement techniques, enabling faster and more efficient selection in breeding programs. His most cited work, a 2024 study on lowland tomatoes, has already garnered 5 citations, signaling its timely relevance to precision agriculture and plant phenomics. By bridging computer vision and agronomy, Dirpan is helping to pave the way for smarter, data-informed crop management, making his research particularly valuable for students and researchers interested in the future of sustainable and high-yield agriculture.
Research Focus
Key Achievements
Top Papers
- 1