Yusuf Hendrawan
Papers
2
Total Citations
8
H-Index
2
About
Yusuf Hendrawan is a researcher specializing in agricultural engineering, precision agriculture, and the application of artificial intelligence to crop management. His work bridges deep learning and controlled-environment agriculture, with a focus on improving efficiency in fruit detection and automated plant production systems. His most-cited paper, "Comparative study of citrus fruits (Citrus reticulata Blanco cv. Batu 55) detection and counting with single and double labels based on convolutional neural network using YOLOv7" (2024, 5 citations), demonstrates his expertise in computer vision for agriculture. This study systematically compares YOLOv7 architectures—original, tiny, and x versions—for detecting and counting citrus fruits, finding that the original YOLOv7 model outperforms its variants. This work contributes to the development of robust, real-time fruit monitoring systems that can enhance yield estimation and harvesting automation. Additionally, his earlier research, "Development of a fully controlled plant factory for moss mat production" (2012, 3 citations), showcases his work in integrating intelligent irrigation, robotic transport, and precision lighting to create fully automated growth environments. These contributions highlight Hendrawan’s role in advancing smart farming technologies that combine AI-driven sensing with automated control systems, offering scalable solutions for modern agriculture. His research is particularly valuable for students and researchers interested in the intersection of deep learning, robotics, and sustainable crop production.
Research Focus
Key Achievements
Top Papers
- 1
- 2