In-field Crop Row Stereo-reconstruction for Plant Phenotyping
Year: 2014
Type: article
Abstract: . This article presents a methodology for crop row structure characterization developed for phenotyping purpose. It is based on 3D modeling performed using Structure from Motion. The model provides an accurate crop row reconstruction and serves as a basis to retrieve plant height and leaf area. To do so, a robust discrimination method using both color and height information is proposed to separate vegetation from soil. The point cloud is scaled and the plant surface is approximated by a triangular mesh. The efficiency of our method was assessed with two data sets collected in outdoor conditions. Height estimation was extremely accurate since both average error and error of reference measurement had the same order of magnitude. Strong correlations and low errors were also obtained for leaf area estimation. The proposed method thus provides an interesting tool for phenotyping.
Authors: Sylvain Jay, Gilles Rabatel, Nathalie Gorretta
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W2740167344