Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images
Responsible for food production and industry inputs, agriculture needs to adapt to worldwide increasing demands and environmental requirements. In this scenario, black oat has gained environmental and economic importance since it can be used in no-tillage systems, green manure, or animal feed supple...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1363 |
| _version_ | 1797614414199783424 |
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| author | Lucas Renato Trevisan Lisiane Brichi Tamara Maria Gomes Fabrício Rossi |
| author_facet | Lucas Renato Trevisan Lisiane Brichi Tamara Maria Gomes Fabrício Rossi |
| author_sort | Lucas Renato Trevisan |
| collection | DOAJ |
| description | Responsible for food production and industry inputs, agriculture needs to adapt to worldwide increasing demands and environmental requirements. In this scenario, black oat has gained environmental and economic importance since it can be used in no-tillage systems, green manure, or animal feed supplementation. Despite its importance, few studies have been conducted to introduce more accurate and technological applications. Plant height (H) correlates with biomass production, which is related to yield. Similarly, productivity status can be estimated from vegetation indices (VIs). The use of unmanned aerial vehicles (UAV) for imaging enables greater spatial and temporal resolutions from which to derive information such as H and VI. However, faster and more accurate methodologies are necessary for the application of this technology. This study intended to obtain high-quality digital surface models (DSMs) and orthoimages from UAV-based RGB images via a direct-to-process means; that is, without the use of ground control points or image pre-processing. DSMs and orthoimages were used to derive H (H<sub>DSM</sub>) and VIs (VI<sub>RGB</sub>), which were used for H and dry biomass (DB) modeling. Results showed that H<sub>DSM</sub> presented a strong correlation with actual plant height (H<sub>REF</sub>) (R<sup>2</sup> = 0.85). Modeling biomass based on H<sub>DSM</sub> demonstrated better performance for data collected up until and including the grain filling (R<sup>2</sup> = 0.84) and flowering (R<sup>2</sup> = 0.82) stages. Biomass modeling based on VI<sub>RGB</sub> performed better for data collected up until and including the booting stage (R<sup>2</sup> = 0.80). The best results for biomass estimation were obtained by combining H<sub>DSM</sub> and VI<sub>RGB</sub>, with data collected up until and including the grain filling stage (R<sup>2</sup> = 0.86). Therefore, the presented methodology has permitted the generation of trustworthy models for estimating the H and DB of black oats. |
| first_indexed | 2024-03-11T07:11:03Z |
| format | Article |
| id | doaj.art-02a0d1dc67b448ee95161ba1689fa961 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:11:03Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-02a0d1dc67b448ee95161ba1689fa9612023-11-17T08:32:02ZengMDPI AGRemote Sensing2072-42922023-02-01155136310.3390/rs15051363Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial ImagesLucas Renato Trevisan0Lisiane Brichi1Tamara Maria Gomes2Fabrício Rossi3“Luiz de Queiroz” College of Agriculture, University of São Paulo, 11 Pádua Dias Avenue, São Paulo 13418-900, Brazil“Luiz de Queiroz” College of Agriculture, University of São Paulo, 11 Pádua Dias Avenue, São Paulo 13418-900, Brazil“Luiz de Queiroz” College of Agriculture, University of São Paulo, 11 Pádua Dias Avenue, São Paulo 13418-900, Brazil“Luiz de Queiroz” College of Agriculture, University of São Paulo, 11 Pádua Dias Avenue, São Paulo 13418-900, BrazilResponsible for food production and industry inputs, agriculture needs to adapt to worldwide increasing demands and environmental requirements. In this scenario, black oat has gained environmental and economic importance since it can be used in no-tillage systems, green manure, or animal feed supplementation. Despite its importance, few studies have been conducted to introduce more accurate and technological applications. Plant height (H) correlates with biomass production, which is related to yield. Similarly, productivity status can be estimated from vegetation indices (VIs). The use of unmanned aerial vehicles (UAV) for imaging enables greater spatial and temporal resolutions from which to derive information such as H and VI. However, faster and more accurate methodologies are necessary for the application of this technology. This study intended to obtain high-quality digital surface models (DSMs) and orthoimages from UAV-based RGB images via a direct-to-process means; that is, without the use of ground control points or image pre-processing. DSMs and orthoimages were used to derive H (H<sub>DSM</sub>) and VIs (VI<sub>RGB</sub>), which were used for H and dry biomass (DB) modeling. Results showed that H<sub>DSM</sub> presented a strong correlation with actual plant height (H<sub>REF</sub>) (R<sup>2</sup> = 0.85). Modeling biomass based on H<sub>DSM</sub> demonstrated better performance for data collected up until and including the grain filling (R<sup>2</sup> = 0.84) and flowering (R<sup>2</sup> = 0.82) stages. Biomass modeling based on VI<sub>RGB</sub> performed better for data collected up until and including the booting stage (R<sup>2</sup> = 0.80). The best results for biomass estimation were obtained by combining H<sub>DSM</sub> and VI<sub>RGB</sub>, with data collected up until and including the grain filling stage (R<sup>2</sup> = 0.86). Therefore, the presented methodology has permitted the generation of trustworthy models for estimating the H and DB of black oats.https://www.mdpi.com/2072-4292/15/5/1363image analysisplant growthcomputer visionremote sensingvisible spectrumunmanned aerial vehicle |
| spellingShingle | Lucas Renato Trevisan Lisiane Brichi Tamara Maria Gomes Fabrício Rossi Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images Remote Sensing image analysis plant growth computer vision remote sensing visible spectrum unmanned aerial vehicle |
| title | Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images |
| title_full | Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images |
| title_fullStr | Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images |
| title_full_unstemmed | Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images |
| title_short | Estimating Black Oat Biomass Using Digital Surface Models and a Vegetation Index Derived from RGB-Based Aerial Images |
| title_sort | estimating black oat biomass using digital surface models and a vegetation index derived from rgb based aerial images |
| topic | image analysis plant growth computer vision remote sensing visible spectrum unmanned aerial vehicle |
| url | https://www.mdpi.com/2072-4292/15/5/1363 |
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