Abstract
There are only a few studies that have been made on accuracy assessments of Leaf Area Index (LAI) and biomass estimation using three-dimensional (3D) models generated by structure from motion (SfM) image processing. In this study, sweet potato was grown with different amounts of nitrogen fertilization in ridge cultivation at an experimental farm. Three-dimensional dense point cloud models were constructed from a series of two-dimensional (2D) color images measured by a small unmanned aerial vehicle (UAV) paired with SfM image processing. Although it was in the early stage of cultivation, a complex ground surface model for ridge cultivation with vegetation was generated, and the uneven ground surface could be estimated with an accuracy of 1.4 cm. Furthermore, in order to accurately estimate growth parameters from the early growth to the harvest period, a 3D model was constructed using a root mean square error (RMSE) of 3.3 cm for plant height estimation. By using a color index, voxel models were generated and LAIs were estimated using a regression model with an RMSE accuracy of 0.123. Further, regression models were used to estimate above-ground and below-ground biomass, or tuberous root weights, based on estimated LAIs.
Highlights
The importance of measuring plant structures, plant functions, and growth parameters is widely recognized in agricultural production and microclimate control [1,2]
The structure from motion (SfM) method, a passive method to construct a 3D model from a series of 2D color images taken with a consumer grade single-lens reflex camera mounted on a unmanned aerial vehicle (UAV), was utilized to generate 3D models of ground surfaces and plant communities
The results showed that the estimation of errors for Leaf Area Index (LAI) were an R2 of 0.57 and an root mean square error (RMSE) of 0.236
Summary
The importance of measuring plant structures, plant functions, and growth parameters is widely recognized in agricultural production and microclimate control [1,2]. Non-destructive 2D remote sensing methods, which have a rapid response time and wide aerial coverage, have been used for growth analysis, plant structure analysis, assessment of environmental response, and estimation of biomass production under different seasonal conditions. Because of sensor positioning and the flight altitude of the sensor platforms, plant communities and plant functions with complex three-dimensional structures are difficult to measure using 2D remote sensing. For all these reasons, 3D remote sensing is a highly superior form of measurement
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