Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques
Multispectral (MS) images offer essential spectral information for monitoring paddy crops’ Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring a...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-01-01
|
| Series: | Geocarto International |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2364725 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846129201773543424 |
|---|---|
| author | Sudarsan Biswal Navneet Pathak Chandranath Chatterjee Damodhara Rao Mailapalli |
| author_facet | Sudarsan Biswal Navneet Pathak Chandranath Chatterjee Damodhara Rao Mailapalli |
| author_sort | Sudarsan Biswal |
| collection | DOAJ |
| description | Multispectral (MS) images offer essential spectral information for monitoring paddy crops’ Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring accuracy. This study focuses to estimate AGB of paddy crop by exploring the combined potential of spectral and textural features of unmanned aerial vehicle (UAV)-MS images using linear regression (LR), multi-linear regression (MLR), and random forest (RF) models. Results demonstrate that near infrared (NIR)-based VIs outperform Colour-Indices. Normalised difference texture indices (NDTIs) composed of NIR, red-edge (RE) and blue (B) bands outperform all-evaluated VIs and grey-level co-occurrence matrix (GLCM)-textures for different growth stages. Combining VIs and NDTIs, RF performs best compared to other models. The outcomes suggest that the combined spectral and texture information can significantly improve estimation of AGB in paddy crops compared to using either of them alone. |
| format | Article |
| id | doaj-art-d1ecc4fdaf6041c98c3a8331af0fc03f |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-d1ecc4fdaf6041c98c3a8331af0fc03f2024-12-10T08:23:09ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2364725Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniquesSudarsan Biswal0Navneet Pathak1Chandranath Chatterjee2Damodhara Rao Mailapalli3Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, IndiaAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, IndiaMultispectral (MS) images offer essential spectral information for monitoring paddy crops’ Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring accuracy. This study focuses to estimate AGB of paddy crop by exploring the combined potential of spectral and textural features of unmanned aerial vehicle (UAV)-MS images using linear regression (LR), multi-linear regression (MLR), and random forest (RF) models. Results demonstrate that near infrared (NIR)-based VIs outperform Colour-Indices. Normalised difference texture indices (NDTIs) composed of NIR, red-edge (RE) and blue (B) bands outperform all-evaluated VIs and grey-level co-occurrence matrix (GLCM)-textures for different growth stages. Combining VIs and NDTIs, RF performs best compared to other models. The outcomes suggest that the combined spectral and texture information can significantly improve estimation of AGB in paddy crops compared to using either of them alone.https://www.tandfonline.com/doi/10.1080/10106049.2024.2364725Aboveground biomassUAV-multispectral imagesvegetation-indicesnormalised difference texture indices (NDTIs), paddy crop |
| spellingShingle | Sudarsan Biswal Navneet Pathak Chandranath Chatterjee Damodhara Rao Mailapalli Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques Geocarto International Aboveground biomass UAV-multispectral images vegetation-indices normalised difference texture indices (NDTIs), paddy crop |
| title | Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques |
| title_full | Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques |
| title_fullStr | Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques |
| title_full_unstemmed | Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques |
| title_short | Estimation of aboveground biomass from spectral and textural characteristics of paddy crop using UAV-multispectral images and machine learning techniques |
| title_sort | estimation of aboveground biomass from spectral and textural characteristics of paddy crop using uav multispectral images and machine learning techniques |
| topic | Aboveground biomass UAV-multispectral images vegetation-indices normalised difference texture indices (NDTIs), paddy crop |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2364725 |
| work_keys_str_mv | AT sudarsanbiswal estimationofabovegroundbiomassfromspectralandtexturalcharacteristicsofpaddycropusinguavmultispectralimagesandmachinelearningtechniques AT navneetpathak estimationofabovegroundbiomassfromspectralandtexturalcharacteristicsofpaddycropusinguavmultispectralimagesandmachinelearningtechniques AT chandranathchatterjee estimationofabovegroundbiomassfromspectralandtexturalcharacteristicsofpaddycropusinguavmultispectralimagesandmachinelearningtechniques AT damodhararaomailapalli estimationofabovegroundbiomassfromspectralandtexturalcharacteristicsofpaddycropusinguavmultispectralimagesandmachinelearningtechniques |