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A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops

Colorado, Julian D. and Calderon, Francisco and Mendez, Diego and Petro, Eliel and Rojas, Juan P. and Correa, Edgar S. and Mondragon, Ivan F. and Rebolledo, Maria Camila and Jaramillo-Botero, Andres (2020) A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops. PLoS ONE, 15 (10). Art. No. e0239591. ISSN 1932-6203. https://resolver.caltech.edu/CaltechAUTHORS:20201006-141547745

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1371/journal.pone.0239591DOIArticle
https://doi.org/10.1371/journal.pone.0239591.s001DOIS1 Video
https://doi.org/10.1371/journal.pone.0239591.s002DOIS1 File
https://doi.org/10.1371/journal.pone.0239591.s003DOIS2 File
ORCID:
AuthorORCID
Colorado, Julian D.0000-0002-6925-0126
Calderon, Francisco0000-0001-8681-415X
Correa, Edgar S.0000-0002-1477-6825
Jaramillo-Botero, Andres0000-0003-2844-0756
Additional Information:© 2020 Colorado et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: June 12, 2020; Accepted: September 9, 2020; Published: October 5, 2020. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. This study was funded by the Optimización Multiescala In-silico de Cultivos Agrícolas Sostenibles (OMICAS) program (Infraestructura y validación en Arroz y Caña de Azúcar), anchored at the Pontificia Universidad Javeriana in Cali and funded within the Colombian Scientific Ecosystem by The World Bank, the Colombian Ministry of Science, Technology and Innovation, the Colombian Ministry of Education and the Colombian Ministry of Industry and Turism, and ICETEX, in the form of a grant awarded to AJB and JC (FP44842-217-2018). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist. The authors would like to thank all CIAT staff that supported the experiments over the crops located at CIAT headquarters in Palmira, Valle del Cauca, Colombia; in particular to Yolima Ospina and Cecile Grenier for their support in upland and lowland trials. Author Contributions: Conceptualization: Julian D. Colorado, Diego Mendez, Eliel Petro, Edgar S. Correa, Ivan F. Mondragon, Maria Camila Rebolledo. Data curation: Julian D. Colorado, Francisco Calderon, Diego Mendez, Edgar S. Correa, Ivan F. Mondragon. Formal analysis: Julian D. Colorado, Francisco Calderon, Juan P. Rojas, Edgar S. Correa, Andres Jaramillo-Botero. Investigation: Julian D. Colorado, Francisco Calderon, Juan P. Rojas, Edgar S. Correa, Ivan F. Mondragon, Maria Camila Rebolledo, Andres Jaramillo-Botero. Methodology: Julian D. Colorado, Diego Mendez, Ivan F. Mondragon, Maria Camila Rebolledo. Project administration: Julian D. Colorado, Andres Jaramillo-Botero. Supervision: Julian D. Colorado, Maria Camila Rebolledo, Andres Jaramillo-Botero. Validation: Julian D. Colorado, Eliel Petro. Writing – original draft: Julian D. Colorado, Francisco Calderon. Writing – review & editing: Diego Mendez, Juan P. Rojas, Ivan F. Mondragon, Maria Camila Rebolledo, Andres Jaramillo-Botero.
Funders:
Funding AgencyGrant Number
Pontificia Universidad JaverianaUNSPECIFIED
Colombian Scientific EcosystemUNSPECIFIED
World BankUNSPECIFIED
Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)UNSPECIFIED
Ministerio de Educación Nacional (Colombia)UNSPECIFIED
Ministerio de Comercio, Industria y Turismo (Colombia)FP44842-217-2018
Instituto Colombiano de Crédito Educativo y Estudios Técnicos en el Exterior (ICETEX)UNSPECIFIED
Issue or Number:10
Record Number:CaltechAUTHORS:20201006-141547745
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201006-141547745
Official Citation:Colorado JD, Calderon F, Mendez D, Petro E, Rojas JP, Correa ES, et al. (2020) A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops. PLoS ONE 15(10): e0239591. https://doi.org/10.1371/journal.pone.0239591
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105845
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Oct 2020 00:12
Last Modified:07 Oct 2020 00:12

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