Abstract
The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R2 of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R2 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage.
Highlights
Unmanned Aircraft System (UAS)-based remote sensing technology has demonstrated the potential for precision agriculture by providing data at high temporal and spatial resolution [1]
Field-based plant measurements showed that plant height (PH) at the capsule development stage is the strongest predictor for capsule volume (CV) estimation, based on the stages assessed in this study (Figure 5)
The highest correlation of PH with CV was observed at capsule formation stage (R2 of 0.71) in Transect 1 (T1), whereas, at capsule development and flowering stage the correlations were lower (R2 0.69 and 0.67 respectively)
Summary
UAS-based remote sensing technology has demonstrated the potential for precision agriculture by providing data at high temporal and spatial resolution [1]. Recently more attention has been given to the use of crop height modelling for yield estimation [10,11,12]. In this context, several studies have shown that crop productivity, in certain crop types In the case of poppy crops, only a single study has looked at the use of remote sensing methods, in which crop height and Leaf Area Index (LAI) were found to be two suitable indicators for estimating opium yield [21]
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