Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform
Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yi...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/120227 |
| _version_ | 1855526794315169792 |
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| author | Brewer, K. Clulow, A.D. Sibanda, M. Gokool, S. Odindi, J. Mutanga, O. Naiken, V. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe |
| author_browse | Brewer, K. Chimonyo, Vimbayi Grace Petrova Clulow, A.D. Gokool, S. Mabhaudhi, Tafadzwanashe Mutanga, O. Naiken, V. Odindi, J. Sibanda, M. |
| author_facet | Brewer, K. Clulow, A.D. Sibanda, M. Gokool, S. Odindi, J. Mutanga, O. Naiken, V. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe |
| author_sort | Brewer, K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules. |
| format | Journal Article |
| id | CGSpace120227 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1202272025-12-08T10:29:22Z Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform Brewer, K. Clulow, A.D. Sibanda, M. Gokool, S. Odindi, J. Mutanga, O. Naiken, V. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe crop growth stage maize temperature measurement stomatal conductance estimation water stress thermal infrared imagery unmanned aerial vehicles machine learning forecasting models precision agriculture smallholders small-scale farming crop water use indicators Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules. 2022-07-08 2022-07-20T08:26:01Z 2022-07-20T08:26:01Z Journal Article https://hdl.handle.net/10568/120227 en Open Access MDPI Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Odindi, J.; Mutanga, O.; Naiken, V.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2022. Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform. Drones, 6(7):169. [doi: https://doi.org/10.3390/drones6070169] |
| spellingShingle | crop growth stage maize temperature measurement stomatal conductance estimation water stress thermal infrared imagery unmanned aerial vehicles machine learning forecasting models precision agriculture smallholders small-scale farming crop water use indicators Brewer, K. Clulow, A.D. Sibanda, M. Gokool, S. Odindi, J. Mutanga, O. Naiken, V. Chimonyo, Vimbayi Grace Petrova Mabhaudhi, Tafadzwanashe Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform |
| title | Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform |
| title_full | Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform |
| title_fullStr | Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform |
| title_full_unstemmed | Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform |
| title_short | Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform |
| title_sort | estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an unmanned aerial vehicle uav platform |
| topic | crop growth stage maize temperature measurement stomatal conductance estimation water stress thermal infrared imagery unmanned aerial vehicles machine learning forecasting models precision agriculture smallholders small-scale farming crop water use indicators |
| url | https://hdl.handle.net/10568/120227 |
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