Sambuddha Ghosal
Papers
1
Total Citations
58
H-Index
1
About
Sambuddha Ghosal is a researcher at the forefront of applying machine learning and computer vision to agricultural science, with a primary focus on plant phenotyping and precision agriculture. His most cited work, "Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications" (2021, 58 citations), introduces a novel deep learning approach that fuses multiple image views to automate soybean pod counting, directly addressing a critical bottleneck in plant breeding programs. This contribution enables more efficient and accurate genotype seed yield ranking, replacing labor-intensive manual methods. Ghosal’s research bridges the gap between advanced artificial intelligence and practical agricultural challenges, demonstrating how deep learning can accelerate crop improvement and food security efforts. His work is particularly notable for its direct application in breeding pipelines, where reliable yield estimation is essential for cultivar development. By developing scalable, image-based solutions for trait measurement, Ghosal is helping to usher in a new era of data-driven agriculture, making him a key figure in the intersection of computer vision and plant science.
Research Focus
Key Achievements
Top Papers
- 1