Dimas Firmanda Al Riza
Papers
1
Total Citations
5
H-Index
1
About
Dimas Firmanda Al Riza is a researcher at the forefront of applying deep learning to precision agriculture, with a particular focus on fruit detection and counting. His work centers on developing and optimizing convolutional neural network architectures—especially the YOLOv7 family—to address real-world challenges in horticulture. In his most-cited study, Al Riza conducted a comparative analysis of YOLOv7, YOLOv7-tiny, and YOLOv7-x for detecting and counting Batu 55 citrus fruits, demonstrating that the original YOLOv7 model outperformed its variants in accuracy. This contribution is pivotal for automating yield estimation and crop monitoring, directly supporting more efficient and data-driven farming practices. With 5 citations to this key paper, his research is gaining traction among agricultural engineers and computer vision specialists. Al Riza’s work exemplifies the growing intersection of artificial intelligence and agriculture, offering scalable solutions that reduce manual labor and improve precision. His findings provide a practical benchmark for deploying deep learning models in complex orchard environments, making him a notable emerging voice in smart farming innovation.
Research Focus
Key Achievements
Top Papers
- 1