David Miron

About

David Miron is a researcher at the forefront of agricultural robotics and computer vision, specializing in real-time object detection for complex, outdoor environments. His work addresses the critical challenge of enabling machines to perceive and interact with dynamic pastoral and agricultural landscapes. Miron’s major contribution lies in the development of the Colour Feature Extreme Learning Machine (CF-ELM) and its advanced iterations. His most cited work, the Multiple-Expert Colour Feature Extreme Learning Machine (MEC-ELM), published in 2018, has garnered 45 citations for its robust approach to real-time detection. Building on this, his 2021 study introduces the Segmented Colour Feature Extreme Learning Machine (SCF-ELM), an ensemble learning algorithm inspired by the Extreme Learning Machine (ELM) that leverages rapid training and inference times. This innovation is particularly significant for agricultural robotics, where speed and accuracy are paramount. With a total of over 75 citations across his key papers, Miron’s research is shaping the future of autonomous systems in agriculture, offering scalable solutions for tasks ranging from livestock monitoring to crop management.

Research Focus

Key Achievements

Top Papers

1. 1
   Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)

   45 citations · 2018
2. 2
   Fast object detection in pastoral landscapes using a Colour Feature Extreme Learning Machine

   20 citations · 2017
3. 3
   The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

   11 citations · 2021

Key Collaborators

Contact & Links