Muh Farid

Papers

Total Citations

H-Index

About

Muh Farid 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 in lowland horticulture. His most cited work, "Combining Image-Based Phenotyping and Multivariate Analysis to Estimate Fruit Fresh Weight in Segregation Lines of Lowland Tomatoes" (2024, 5 citations), addresses a critical challenge in plant breeding: the need for non-destructive, high-throughput methods to assess fruit weight. By integrating computer vision with statistical modeling, Farid demonstrates how breeders and farmers can accurately predict marketable production without physically harvesting or damaging fruit—a significant advancement over conventional destructive measurements. This approach not only accelerates selection in tomato breeding programs but also reduces labor and resource waste. Farid’s work is particularly valuable for lowland agriculture, where environmental stresses often complicate yield estimation. His research bridges the gap between digital agriculture and practical breeding, offering scalable solutions for food security. With growing interest in precision phenotyping, Farid’s contributions are poised to influence both academic research and applied crop improvement, making him a key figure in the intersection of data science and plant biology.