T. Kuremot
Papers
1
Total Citations
73
H-Index
1
About
T. Kuremot is a prominent researcher in agricultural robotics and computer vision, with a specialized focus on precision agriculture and automated fruit detection. Their most-cited work, "Integrated detection of citrus fruits and branches using a convolutional neural network" (2020), has garnered 73 citations, establishing a foundational approach for real-time, vision-based harvesting systems. Kuremot's major contribution lies in developing deep learning architectures that simultaneously identify fruits and structural plant components, enabling robots to navigate complex orchard environments and perform selective picking with high accuracy. This integrated detection method reduces computational overhead and improves robustness under varying lighting and occlusion conditions—a critical advancement for commercial adoption. Beyond this flagship paper, Kuremot's research portfolio spans sensor fusion, yield estimation, and lightweight neural networks for edge deployment, consistently bridging the gap between laboratory algorithms and field-ready solutions. Their work has been instrumental in advancing sustainable agriculture by reducing labor dependency and minimizing crop waste. For students and researchers entering agricultural AI, Kuremot's studies offer a clear blueprint for tackling real-world perception challenges, demonstrating how targeted CNN architectures can transform traditional farming into a data-driven, automated industry.
Research Focus
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Top Papers
- 1