Javad Mohammadpour Velni
Papers
9
Total Citations
218
H-Index
7
About
Javad Mohammadpour Velni is a leading researcher at the intersection of precision agriculture, multi-robot systems, and artificial intelligence. His work focuses on developing autonomous ground vehicles and deep learning solutions to transform agricultural practices, particularly in plant phenotyping and field coverage. His most cited paper, "Real-Time Plant Leaf Counting Using Deep Object Detection Networks" (2020, 80 citations), pioneered the use of deep neural networks for rapid, accurate plant trait analysis, addressing a critical bottleneck in high-throughput phenotyping. Velni has also made foundational contributions to multi-agent coverage control, introducing graph-theoretic and reinforcement learning-based approaches for deploying heterogeneous robot teams in precision agriculture. His 2018 paper on "Coverage Control with Multiple Ground Robots for Precision Agriculture" (23 citations) and the related "Development of an Autonomous Ground Robot for Field High Throughput Phenotyping" (23 citations) demonstrate his commitment to bridging theory and practice. More recently, his 2024 work on "Learning-Based Safety Critical Model Predictive Control Using Stochastic Control Barrier Functions" (4 citations) extends his expertise into safe autonomous navigation under uncertainty. With over 200 total citations, Velni’s research is shaping the future of smart farming, offering scalable, data-driven solutions for sustainable agriculture.
Research Focus
Key Achievements
Top Papers
- 1Real-Time Plant Leaf Counting Using Deep Object Detection Networks80 citations · 2020
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- 4Coverage Control with Multiple Ground Robots for Precision Agriculture23 citations · 2018
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- 6Coverage control of moving sensor networks with multiple regions of interest14 citations · 2017
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- 9Agricultural Field Coverage Using Cooperating Unmanned Ground Vehicles3 citations · 2019