Mochamad Bagus Hermanto
Papers
2
Total Citations
8
H-Index
2
About
Mochamad Bagus Hermanto is a researcher at the forefront of agricultural technology, specializing in precision farming, computer vision, and controlled-environment agriculture. His work bridges the gap between deep learning and practical horticulture, with a focus on automating fruit detection and optimizing plant production systems. In his most-cited study (2024, 5 citations), Hermanto compared YOLOv7 architectures for detecting and counting Batu 55 citrus fruits, demonstrating that the original YOLOv7 model outperforms its tiny and x variants in accuracy—a critical insight for deploying real-time yield estimation in orchards. Earlier, he contributed to the development of a fully controlled plant factory for moss mat production (2012, 3 citations), integrating intelligent irrigation, robotic transporters, and precision artificial lighting to create a scalable, automated growth environment. This work showcases his ability to combine IoT, robotics, and lighting control for sustainable agriculture. Hermanto’s research is instrumental for students and engineers seeking to apply AI and automation to real-world farming challenges, offering both foundational methods and practical deployment strategies.
Research Focus
Key Achievements
Top Papers
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