Miguel Garrido Izard

About

Miguel Garrido Izard is a researcher specializing in agricultural robotics, precision phenotyping, and 3D sensing technologies applied to crop monitoring. His work sits at the intersection of computer vision, LiDAR sensing, and plant science, with a particular focus on developing automated methods for analyzing maize crops in real-world agricultural environments. Garrido Izard has made significant contributions to the field of plant phenotyping by pioneering the use of consumer-grade time-of-flight cameras — notably the Microsoft Kinect v2 — mounted on robotic platforms to reconstruct detailed 3D models of individual maize plants. His research has addressed critical agronomic parameters including stem position, plant height, and leaf area estimation, enabling cost-effective and scalable crop assessment. Complementing this work, he has also advanced LiDAR-based approaches for single plant detection and clustering, demonstrating robust performance across varied agricultural settings. His most cited paper, on stem position and height determination using time-of-flight cameras (38 citations), reflects the practical relevance of his methods to both farm management and crop breeding programs. Collectively, his publications have garnered over 100 citations, underlining their influence on the growing field of agricultural robotics and automated crop phenotyping — a discipline increasingly vital to feeding a growing global population efficiently and sustainably.

Research Focus

Key Achievements

Top Papers

1. 1
   Determination of stem position and height of reconstructed maize plants using a time-of-flight camera

   38 citations · 2018
2. 2
   Iterative individual plant clustering in maize with assembled 2D LiDAR data

   32 citations · 2018
3. 3
   Leaf Area Estimation of Reconstructed Maize Plants Using a Time-of-Flight Camera Based on Different Scan Directions

   19 citations · 2018
4. 4
   Reconstruction of geo-referenced maize plants using a con-sumer time-of-flight camera in different agricultural envi-ronments

   6 citations · 2016
5. 5
   Using Assembled 2D LiDAR Data for Single Plant Detection

   5 citations · 2016
6. 6
   Clustering of Laser Scanner Perception Points of Maize Plants

   4 citations · 2017
7. 7
   Reconstruction of geo-referenced maize plants using a consumer time-of-flight camera in different agricultural environments.

   3 citations · 2016

Key Collaborators

Contact & Links