A5090802018- Remote Sensing and LiDAR Applications
- Smart Agriculture and AI
- Remote Sensing in Agriculture
- Remote-Sensing Image Classification
- Advanced Image and Video Retrieval Techniques
- Leaf Properties and Growth Measurement
- Greenhouse Technology and Climate Control
Universidad de Ibagué
2020-2022
Currently, there are no free databases of 3D point clouds and images for seedling phenotyping. Therefore, this paper describes a platform scanning using Lidar with which database was acquired use in plant phenotyping research. In total, 362 maize seedlings were recorded an RGB camera SICK LMS4121R-13000 laser scanner angular resolutions 45° 0.5° respectively. The scanned plants diverse, captures ranging from less than 10 cm to 40 cm, 7 24 days after planting different light conditions indoor...
Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors detectors allows to find distinctive keypoints an the this approach for agronomical applications become a widespread field study. By combining near infrared (NIR) images, acquired with modified Nikon D80 camera, visible spectrum (VIS) D300s, proper crop identification could be obtained. Still, different sensors brings matching challenge due difference...
Registration is a technique employed for the alignment of point clouds in single coordinate system. This process very useful reconstruction 3D plant models, extraction their morphological features and subsequent analysis phenotype. One most widely studied recording algorithms ICP (Iterative Closest Point), which based on rigid transformations. Although literature there are several comparative studies between different variants ICP, no study with other more recent existing methods principles....