REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES
ABSTRACT Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remote...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Sociedade Brasileira de Engenharia Agrícola
2022-05-01
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| Series: | Engenharia Agrícola |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000200212&tlng=en |
| _version_ | 1811306733481492480 |
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| author | Aderson S. de Andrade Junior Silvestre P. da Silva Ingrid S. Setúbal Henrique A. de Souza Paulo F. de M. J. Vieira |
| author_facet | Aderson S. de Andrade Junior Silvestre P. da Silva Ingrid S. Setúbal Henrique A. de Souza Paulo F. de M. J. Vieira |
| author_sort | Aderson S. de Andrade Junior |
| collection | DOAJ |
| description | ABSTRACT Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans. |
| first_indexed | 2024-04-13T08:50:46Z |
| format | Article |
| id | doaj.art-332b72691d8e4a1abfd759ae33b8ed2c |
| institution | Directory Open Access Journal |
| issn | 0100-6916 |
| language | English |
| last_indexed | 2024-04-13T08:50:46Z |
| publishDate | 2022-05-01 |
| publisher | Sociedade Brasileira de Engenharia Agrícola |
| record_format | Article |
| series | Engenharia Agrícola |
| spelling | doaj.art-332b72691d8e4a1abfd759ae33b8ed2c2022-12-22T02:53:28ZengSociedade Brasileira de Engenharia AgrícolaEngenharia Agrícola0100-69162022-05-0142210.1590/1809-4430-eng.agric.v42n2e20210177/2022REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGESAderson S. de Andrade Juniorhttps://orcid.org/0000-0002-0619-1851Silvestre P. da SilvaIngrid S. SetúbalHenrique A. de SouzaPaulo F. de M. J. VieiraABSTRACT Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000200212&tlng=enGlycine max L.RPAvegetation indexesgas exchange |
| spellingShingle | Aderson S. de Andrade Junior Silvestre P. da Silva Ingrid S. Setúbal Henrique A. de Souza Paulo F. de M. J. Vieira REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES Engenharia Agrícola Glycine max L. RPA vegetation indexes gas exchange |
| title | REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES |
| title_full | REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES |
| title_fullStr | REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES |
| title_full_unstemmed | REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES |
| title_short | REMOTE DETECTION OF WATER AND NUTRITIONAL STATUS OF SOYBEANS USING UAV-BASED IMAGES |
| title_sort | remote detection of water and nutritional status of soybeans using uav based images |
| topic | Glycine max L. RPA vegetation indexes gas exchange |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162022000200212&tlng=en |
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