- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Wheel-Based Lidar Data for Plant Height and Canopy Cover Evaluation to Aid Biomass Prediction
摘要： Biomass estimation is fundamental for a variety of plant ecological studies. Direct measurement of aboveground biomass by clipping and sorting is destructive, time-consuming and laborious, thus reducing the ability of extensive sampling. Various plant traits, such as plant height, canopy cover, and leaf and plant structure contribute towards its biomass. In this study, we focus on exploiting wheel-based LiDAR data over an agricultural field to perform growth monitoring and canopy cover estimation, which would play a crucial role in the future to develop a non-invasive technique for biomass prediction.
关键词： Biomass,plant traits,LiDAR data,plant height,canopy cover
更新于2025-09-23 15:22:29
-
[American Society of Agricultural and Biological Engineers 2018 Detroit, Michigan July 29 - August 1, 2018 - ()] 2018 Detroit, Michigan July 29 - August 1, 2018 - <i>Cotton Yield Estimation based on Plant Height From UAV-based Imagery Data </i>
摘要： Accurate estimation of crop yield before harvest, especially in early growth stages, is important for farmers and researchers to optimize field management and evaluate crop performance. However, conventional methods of using ground sensing to estimate crop yield are not efficient. The goal of this research was to evaluate the potential of using a UAV-based remote sensing system with a low-cost RGB camera to estimate yield of cotton within season. The UAV system took images at 50 m above ground level over a cotton field at the growth stage of first flower. Waypoints and flight speed were selected to allow > 70% image overlap in both forward and side directions. Images were processed to develop a geo-referenced orthomosaic image and a digital elevation model (DEM) of the field, which was then used to map plant height by calculating the difference in elevation between the crop canopy and the bare soil surface. Twelve ground control points (calibration objects) with known GPS coordinates and height were deployed in the field and were used as check points for geo-referencing and height calibration. Geo-referenced yield data were registered with the plant height map row-by-row. Correlation analysis between yield and plant height was conducted row-by-row with row registration and without row registration respectively. Pearson correlation coefficients between yield and plant height for all individual rows were in the range of 66% to 96%, higher than those without row registration (54% to 95%). A non-parametric regression used for building a yield estimation model based on image-derived plant height was able to estimate yield with less than 10% error (root mean square error of 360.4 kg ha-1 and mean absolute error of 180.9 kg ha-1). The results indicated that the UAV-based remote sensing system equipped with a low-cost digital camera was able to estimate cotton yield with acceptable errors.
关键词： yield estimation,UAV-based remote sensing,geo-registration,plant height,Cotton
更新于2025-09-23 15:21:01
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Implementation of UAV-Based Lidar for High Throughput Phenotyping
摘要： High throughput phenotyping is rapidly gaining widespread popularity due to its ability to non-destructively extract plant traits, such as plant height, canopy density, leaf and plant structure, and so on. In this study, we focus on developing a UAV-based LiDAR system to acquire accurate time-series 3D point clouds for monitoring two specific plant traits – plant height and canopy cover – which are integral for enhancing crop genetic improvement to meet the needs of future generations. Furthermore, the obtained estimates are validated by comparing the results with those obtained from wheel-based LiDAR data.
关键词： High throughput phenotyping,UAV,plant height,canopy cover,LiDAR system
更新于2025-09-09 09:28:46