Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging
Unmanned Aerial Vehicle (UAV) platforms and associated sensing technologies are extensively utilized in precision agriculture. Using LiDAR and imaging sensors mounted on multirotor UAVs, we can observe fine-scale crop variations that can help improve fertilizer management and maximize yields. In thi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-10-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0303243420303457 |
| _version_ | 1811317651273678848 |
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| author | Yuri Shendryk Jeremy Sofonia Robert Garrard Yannik Rist Danielle Skocaj Peter Thorburn |
| author_facet | Yuri Shendryk Jeremy Sofonia Robert Garrard Yannik Rist Danielle Skocaj Peter Thorburn |
| author_sort | Yuri Shendryk |
| collection | DOAJ |
| description | Unmanned Aerial Vehicle (UAV) platforms and associated sensing technologies are extensively utilized in precision agriculture. Using LiDAR and imaging sensors mounted on multirotor UAVs, we can observe fine-scale crop variations that can help improve fertilizer management and maximize yields. In this study we used UAV mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials with variable nitrogen (N) fertilization inputs in the Wet Tropics region of Australia. From six surveys performed at 42-day intervals, we monitored crop growth in terms of height, density and vegetation indices. In each survey period, we estimated a set of models to predict at-harvest biomass at fine scale (2m×2m plots). We compared the predictive performance of models based on multispectral predictors only, LiDAR predictors only, a fusion of multispectral and LiDAR predictors, and a normalized difference vegetation index (NDVI) benchmark. We found that predictive performance peaked early in the season, at 100–142 days after the previous harvest (DAH), and declined closer to the harvest date. At peak performance (i.e. 142 DAH), the multispectral model performed slightly better (R¯2=0.57) than the LiDAR model (R¯2=0.52), with both outperforming NDVI benchmark (R¯2=0.34). This, however, flipped later in the season, with LiDAR performing slightly better than the multispectral imaging and NDVI benchmark. Interestingly, the fusion model did not perform significantly better than the multispectral model at 100–142 DAH, nor better than LiDAR in subsequent periods. We also estimated a model to predict contemporaneous leaf N content (%) using multispectral imagery, which demonstrated an R¯2 of 0.57. Our results are of particular interest to nutrient management programs aiming to deliver N fertilizer guidelines for sustainable sugarcane production, as both fine-scale biomass and leaf N content predictions are feasible when management interventions are still possible (i.e. as early as at 100 DAH). |
| first_indexed | 2024-04-13T12:11:59Z |
| format | Article |
| id | doaj.art-c9b319e9eacf420ab553092100ffb44f |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-13T12:11:59Z |
| publishDate | 2020-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-c9b319e9eacf420ab553092100ffb44f2022-12-22T02:47:28ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322020-10-0192102177Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imagingYuri Shendryk0Jeremy Sofonia1Robert Garrard2Yannik Rist3Danielle Skocaj4Peter Thorburn5CSIRO, Agriculture and Food, Brisbane Australia; Corresponding author.Emesent, Brisbane, AustraliaCSIRO, Land and Water, Brisbane, AustraliaCSIRO, Agriculture and Food, Brisbane AustraliaSugar Research Australia, Tully, AustraliaCSIRO, Agriculture and Food, Brisbane AustraliaUnmanned Aerial Vehicle (UAV) platforms and associated sensing technologies are extensively utilized in precision agriculture. Using LiDAR and imaging sensors mounted on multirotor UAVs, we can observe fine-scale crop variations that can help improve fertilizer management and maximize yields. In this study we used UAV mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials with variable nitrogen (N) fertilization inputs in the Wet Tropics region of Australia. From six surveys performed at 42-day intervals, we monitored crop growth in terms of height, density and vegetation indices. In each survey period, we estimated a set of models to predict at-harvest biomass at fine scale (2m×2m plots). We compared the predictive performance of models based on multispectral predictors only, LiDAR predictors only, a fusion of multispectral and LiDAR predictors, and a normalized difference vegetation index (NDVI) benchmark. We found that predictive performance peaked early in the season, at 100–142 days after the previous harvest (DAH), and declined closer to the harvest date. At peak performance (i.e. 142 DAH), the multispectral model performed slightly better (R¯2=0.57) than the LiDAR model (R¯2=0.52), with both outperforming NDVI benchmark (R¯2=0.34). This, however, flipped later in the season, with LiDAR performing slightly better than the multispectral imaging and NDVI benchmark. Interestingly, the fusion model did not perform significantly better than the multispectral model at 100–142 DAH, nor better than LiDAR in subsequent periods. We also estimated a model to predict contemporaneous leaf N content (%) using multispectral imagery, which demonstrated an R¯2 of 0.57. Our results are of particular interest to nutrient management programs aiming to deliver N fertilizer guidelines for sustainable sugarcane production, as both fine-scale biomass and leaf N content predictions are feasible when management interventions are still possible (i.e. as early as at 100 DAH).http://www.sciencedirect.com/science/article/pii/S0303243420303457SugarcaneNitrogenFertilizerUAVDroneLiDAR |
| spellingShingle | Yuri Shendryk Jeremy Sofonia Robert Garrard Yannik Rist Danielle Skocaj Peter Thorburn Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging International Journal of Applied Earth Observations and Geoinformation Sugarcane Nitrogen Fertilizer UAV Drone LiDAR |
| title | Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging |
| title_full | Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging |
| title_fullStr | Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging |
| title_full_unstemmed | Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging |
| title_short | Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging |
| title_sort | fine scale prediction of biomass and leaf nitrogen content in sugarcane using uav lidar and multispectral imaging |
| topic | Sugarcane Nitrogen Fertilizer UAV Drone LiDAR |
| url | http://www.sciencedirect.com/science/article/pii/S0303243420303457 |
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