A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops Article Swipe
Julian D. Colorado
,
Francisco Calderón
,
Diego Méndez
,
E. Petró
,
Juan P. Rojas
,
Edgar S. Correa
,
Iván F. Mondragón
,
Maria Camila Rebolledo
,
Andrés Jaramillo-Botero
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0239591
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0239591
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.
Related Topics
Concepts
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)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0239591
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0239591&type=printable
- OA Status
- gold
- Cited By
- 21
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3091730256
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3091730256Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0239591Digital Object Identifier
- Title
-
A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice cropsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-10-05Full publication date if available
- Authors
-
Julian D. Colorado, Francisco Calderón, Diego Méndez, E. Petró, Juan P. Rojas, Edgar S. Correa, Iván F. Mondragón, Maria Camila Rebolledo, Andrés Jaramillo-BoteroList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0239591Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0239591&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0239591&type=printableDirect OA link when available
- Concepts
-
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)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2, 2023: 4, 2022: 6, 2021: 4Per-year citation counts (last 5 years)
- References (count)
-
42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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