Eka Firmansyah
Papers
1
Total Citations
2
H-Index
1
About
Eka Firmansyah is a researcher whose work sits at the intersection of artificial intelligence, computer vision, and smart agriculture. His key research areas include edge detection, model optimization, and the deployment of deep learning on embedded systems—particularly for agricultural automation. Firmansyah’s major contribution lies in applying Intel’s OpenVINO Toolkit to optimize neural network models for real-time edge detection, enabling lightweight, efficient inference on resource-constrained devices. His most cited paper, “Edge Detection of Strawberries Ripeness Based on Model Optimization Using Intel OpenVINO Toolkit” (2023), demonstrates how model compression and hardware acceleration can bring intelligent harvesting robots closer to practical deployment. This work has already garnered 2 citations, signaling early impact in the precision agriculture community. Firmansyah’s broader research addresses the gap between big data analytics and on-field robotics, focusing on soil data analysis, fertilizer spraying, and fruit-picking automation. By bridging computer vision optimization with agricultural robotics, he contributes to the growing field of smart farming—making autonomous crop management more accessible and efficient for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1