Hemanth Narayan Dakshinamurthy

Papers

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About

Hemanth Narayan Dakshinamurthy is a researcher at the forefront of agricultural robotics and precision sensing technologies. His work primarily focuses on developing autonomous systems for sustainable farming, with key contributions in robotic weeding, deep learning-based weed detection, and soil moisture measurement. His most cited paper, "Frontier: Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems" (2021, 18 citations), highlights his role in advancing compact, intelligent weeding platforms that leverage deep learning for accurate, real-time weed identification and precision actuation. This work addresses critical challenges in reducing herbicide use and improving crop yield. Additionally, his research on "Waveform analysis for short time domain reflectometry (TDR) probes" (2025, 6 citations) introduces innovative methods for calibrated soil moisture measurements from partial sensor insertions, enhancing water management in agriculture. Dakshinamurthy’s contributions are notable for bridging the gap between cutting-edge AI and practical field applications, earning recognition for their potential to revolutionize precision agriculture. His work continues to inspire students and researchers exploring autonomy in detection, actuation, and environmental sensing.