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Evaluating Sugarcane Yield Variability with UAV-Derived Cane Height under Different Water and Nitrogen Conditions
Computer Vision and Pattern Recognition · Submitted 28 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data.
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The abstract, sentence by sentence
This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nitrogen levels (low, medium, and high), with repeated treatments. In pixel distribution of DSM for each block, it provided bimodal distribution representing two peaks, ground level (gaps within canopies) and top of the canopies respectively. Using bimodal distribution, mean cane height was extracted for each block by applying a trimmed mean to the pixel distribution, focusing on the top canopy points. Similarly, the extracted mean elevation of the base was derived from the bottom points, representing ground level. The Derived Cane Height Model (DCHM) was generated by taking the difference between the mean canopy height and mean base elevation for each block. Yield measurements (tons/acre) were recorded post-harvest for each block. By aggregating the data into nine treatment zones (e.g., high water-low nitrogen, low water-high nitrogen), the DCHM and median yield were calculated for each zone. The regression analysis between the DCHM and corresponding yields for the different treatment zones yielded an R 2 of 0.95. This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. | contribution 0.97 | 0.92 | 0.05 | asserted 0.76 |
| s1 | The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nitrogen levels (low, medium, and high), with repeated treatments. | method 1.00 | 0.89 | 0.02 | asserted 1.00 |
| s2 | In pixel distribution of DSM for each block, it provided bimodal distribution representing two peaks, ground level (gaps within canopies) and top of the canopies respectively. | method 0.70 | 0.89 | 0.02 | asserted 1.00 |
| s3 | Using bimodal distribution, mean cane height was extracted for each block by applying a trimmed mean to the pixel distribution, focusing on the top canopy points. | method 1.00 | 0.94 | 0.02 | asserted 1.00 |
| s4 | Similarly, the extracted mean elevation of the base was derived from the bottom points, representing ground level. | method 1.00 | 0.93 | 0.02 | asserted 1.00 |
| s5 | The Derived Cane Height Model (DCHM) was generated by taking the difference between the mean canopy height and mean base elevation for each block. | method 1.00 | 0.95 | 0.03 | asserted 0.99 |
| s6 | Yield measurements (tons/acre) were recorded post-harvest for each block. | method 1.00 | 0.93 | 0.02 | asserted 0.99 |
| s7 | By aggregating the data into nine treatment zones (e.g., high water-low nitrogen, low water-high nitrogen), the DCHM and median yield were calculated for each zone. | method 0.98 | 0.95 | 0.05 | asserted 0.98 |
| s8 | The regression analysis between the DCHM and corresponding yields for the different treatment zones yielded an R 2 of 0.95. | result 1.00 | 0.96 | 0.29 | asserted 1.00 |
| s9 | This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data. | interpretation 0.89 | 0.94 | 0.89 | asserted 1.00 |
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The paper
Original abstract
This study investigates the relationship between sugarcane yield and cane height derived under different water and nitrogen conditions from pre-harvest Digital Surface Model (DSM) obtained via Unmanned Aerial Vehicle (UAV) flights over a sugarcane test farm. The farm was divided into 62 blocks based on three water levels (low, medium, and high) and three nitrogen levels (low, medium, and high), with repeated treatments. In pixel distribution of DSM for each block, it provided bimodal distribution representing two peaks, ground level (gaps within canopies) and top of the canopies respectively. Using bimodal distribution, mean cane height was extracted for each block by applying a trimmed mean to the pixel distribution, focusing on the top canopy points. Similarly, the extracted mean elevation of the base was derived from the bottom points, representing ground level. The Derived Cane Height Model (DCHM) was generated by taking the difference between the mean canopy height and mean base elevation for each block. Yield measurements (tons/acre) were recorded post-harvest for each block. By aggregating the data into nine treatment zones (e.g., high water-low nitrogen, low water-high nitrogen), the DCHM and median yield were calculated for each zone. The regression analysis between the DCHM and corresponding yields for the different treatment zones yielded an R 2 of 0.95. This study demonstrates the significant impact of water and nitrogen treatments on sugarcane height and yield, utilizing one-time UAV-derived DSM data.
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