Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils
Wheat production in southern Queensland, Australia is adversely affected by soil sodicity. Crop phenotyping could be useful to improve productivity in such soils. This research focused on adapting high-throughput phenotyping of crop biophysical attributes to monitor crop health, nutrient deficiencie...
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MDPI AG
2020-04-01
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| Online Access: | https://www.mdpi.com/2504-3900/36/1/206 |
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| author | Malini Roy Choudhury Jack Christopher Armando Apan Scott Chapman Neal Menzies Yash Dang |
| author_facet | Malini Roy Choudhury Jack Christopher Armando Apan Scott Chapman Neal Menzies Yash Dang |
| author_sort | Malini Roy Choudhury |
| collection | DOAJ |
| description | Wheat production in southern Queensland, Australia is adversely affected by soil sodicity. Crop phenotyping could be useful to improve productivity in such soils. This research focused on adapting high-throughput phenotyping of crop biophysical attributes to monitor crop health, nutrient deficiencies and plant moisture availability. We conducted an aerial and ground-based campaign during several wheat growing stages to capture crop information for 18 wheat genotypes at a moderately sodic site near Goondiwindi in southern Queensland. Three techniques were employed (multispectral, hyperspectral, and 3D point cloud) to monitor crop characteristics and predict biomass and yield. Spectral information and vegetation indices (VI) such as, normalized different vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), and leaf area index (LAI) were derived from multispectral imagery and compared with ground-based agronomic data for biomass, leaf area, and yield. Significant correlations were observed between NDVI and yield (R<sup>2</sup> = 0.81), LAI (R<sup>2</sup> = 0.74), and biomass (R<sup>2</sup> = 0.65). Partial least square regression (PLS-R) modelling using hyperspectral spectroscopy data provided crop yield predictions that correlated significantly with observed yield (R<sup>2</sup> = 0.65). The 3D point cloud technique was effective with comparison to in field manual measurements of crop architectural traits height and foliage cover (e.g., for height R<sup>2</sup> = 0.73). For, this study multispectral techniques showed a greater potential to predict biomass and yield of wheat genotypes under moderately sodic soils than hyperspectral and 3D point cloud techniques. In future, the genotypes will be tested under more severely sodic soils to monitor crop performance and predicting yield. |
| first_indexed | 2024-03-10T20:35:58Z |
| format | Article |
| id | doaj.art-8e3d5d68dc04497c9c134a5cc65abf12 |
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| issn | 2504-3900 |
| language | English |
| last_indexed | 2024-03-10T20:35:58Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-8e3d5d68dc04497c9c134a5cc65abf122023-11-19T21:00:02ZengMDPI AGProceedings2504-39002020-04-0136120610.3390/proceedings2019036206Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic SoilsMalini Roy Choudhury0Jack Christopher1Armando Apan2Scott Chapman3Neal Menzies4Yash Dang5School of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD 4072, AustraliaQueensland Alliance for Agricultural and Food Innovation, The University of Queensland, Leslie Research Facility, Toowoomba, QLD 4350, AustraliaSchool of Civil Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD 4350, AustraliaSchool of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD 4072, AustraliaSchool of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD 4072, AustraliaSchool of Agriculture and Food Sciences, The University of Queensland, St Lucia, QLD 4072, AustraliaWheat production in southern Queensland, Australia is adversely affected by soil sodicity. Crop phenotyping could be useful to improve productivity in such soils. This research focused on adapting high-throughput phenotyping of crop biophysical attributes to monitor crop health, nutrient deficiencies and plant moisture availability. We conducted an aerial and ground-based campaign during several wheat growing stages to capture crop information for 18 wheat genotypes at a moderately sodic site near Goondiwindi in southern Queensland. Three techniques were employed (multispectral, hyperspectral, and 3D point cloud) to monitor crop characteristics and predict biomass and yield. Spectral information and vegetation indices (VI) such as, normalized different vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), and leaf area index (LAI) were derived from multispectral imagery and compared with ground-based agronomic data for biomass, leaf area, and yield. Significant correlations were observed between NDVI and yield (R<sup>2</sup> = 0.81), LAI (R<sup>2</sup> = 0.74), and biomass (R<sup>2</sup> = 0.65). Partial least square regression (PLS-R) modelling using hyperspectral spectroscopy data provided crop yield predictions that correlated significantly with observed yield (R<sup>2</sup> = 0.65). The 3D point cloud technique was effective with comparison to in field manual measurements of crop architectural traits height and foliage cover (e.g., for height R<sup>2</sup> = 0.73). For, this study multispectral techniques showed a greater potential to predict biomass and yield of wheat genotypes under moderately sodic soils than hyperspectral and 3D point cloud techniques. In future, the genotypes will be tested under more severely sodic soils to monitor crop performance and predicting yield.https://www.mdpi.com/2504-3900/36/1/206wheat genotypesphenotypingvegetation indicesmultispectralhyperspectral3D point cloud |
| spellingShingle | Malini Roy Choudhury Jack Christopher Armando Apan Scott Chapman Neal Menzies Yash Dang Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils Proceedings wheat genotypes phenotyping vegetation indices multispectral hyperspectral 3D point cloud |
| title | Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils |
| title_full | Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils |
| title_fullStr | Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils |
| title_full_unstemmed | Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils |
| title_short | Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils |
| title_sort | integrated high throughput phenotyping with high resolution multispectral hyperspectral and 3d point cloud techniques for screening wheat genotypes on sodic soils |
| topic | wheat genotypes phenotyping vegetation indices multispectral hyperspectral 3D point cloud |
| url | https://www.mdpi.com/2504-3900/36/1/206 |
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