Open AccessA novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops
Author(s) -
Julián Colorado,
Francisco Calderón,
Diego Méndez,
Eliel Petro,
Juan Rojas,
Edgar S. Correa,
Iván F. Mondragón,
María Camila Rebolledo,
Andrés Jaramillo-Botero
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0239591
Subject(s) - multispectral image , segmentation , biomass (ecology) , canopy , artificial intelligence , cluster analysis , image segmentation , computer science , sampling (signal processing) , pattern recognition (psychology) , pixel , remote sensing , environmental science , mathematics , agronomy , computer vision , ecology , biology , geography , filter (signal processing)
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 R 2 = 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.