Your browser doesn't support javascript.
loading
A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops.
Colorado, Julian D; Calderon, Francisco; Mendez, Diego; Petro, Eliel; Rojas, Juan P; Correa, Edgar S; Mondragon, Ivan F; Rebolledo, Maria Camila; Jaramillo-Botero, Andres.
Affiliation
  • Colorado JD; School of Engineering, Pontificia Universidad Javeriana Bogota, Bogota, Colombia.
  • Calderon F; School of Engineering, Pontificia Universidad Javeriana Bogota, Bogota, Colombia.
  • Mendez D; School of Engineering, Pontificia Universidad Javeriana Bogota, Bogota, Colombia.
  • Petro E; The International Center for Tropical Agriculture -CIAT, Palmira, Colombia.
  • Rojas JP; School of Engineering, Pontificia Universidad Javeriana Bogota, Bogota, Colombia.
  • Correa ES; INRAE-AFEF, I2S, LIRMM-ICAR, Université de Montpellier, Montpellier, France.
  • Mondragon IF; School of Engineering, Pontificia Universidad Javeriana Bogota, Bogota, Colombia.
  • Rebolledo MC; School of Engineering, Pontificia Universidad Javeriana Bogota, Bogota, Colombia.
  • Jaramillo-Botero A; The International Center for Tropical Agriculture -CIAT, Palmira, Colombia.
PLoS One ; 15(10): e0239591, 2020.
Article in En | MEDLINE | ID: mdl-33017406
Traditional methods to measure spatio-temporal variations in biomass rely on a labor-intensive destructive sampling of the crop. In this paper, we present a high-throughput phenotyping approach for the estimation of Above-Ground Biomass Dynamics (AGBD) using an unmanned aerial system. Multispectral imagery was acquired and processed by using the proposed segmentation method called GFKuts, that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo based K-means, and a guided image filtering. Accurate plot segmentation results enabled the extraction of several canopy features associated with biomass yield. Machine learning algorithms were trained to estimate the AGBD according to the growth stages of the crop and the physiological response of two rice genotypes under lowland and upland production systems. Results report AGBD estimation correlations with an average of r = 0.95 and R2 = 0.91 according to the experimental data. We compared our segmentation method against a traditional technique based on clustering. A comprehensive improvement of 13% in the biomass correlation was obtained thanks to the segmentation method proposed herein.
Subject(s)

Full text: 1 Database: MEDLINE Main subject: Oryza / Remote Sensing Technology Country/Region as subject: America do sul / Colombia Language: En Journal: PLoS One Year: 2020 Type: Article Affiliation country: Colombia

Full text: 1 Database: MEDLINE Main subject: Oryza / Remote Sensing Technology Country/Region as subject: America do sul / Colombia Language: En Journal: PLoS One Year: 2020 Type: Article Affiliation country: Colombia