Poppy crop capsule volume estimation using UAS remote sensing and random forest regression

Improved prediction of poppy capsule volume is essential for optimal management of poppy crop. In order to estimate poppy capsule volume accurately using remotely sensed imagery, the selection of most appropriate models and predictor variables is essential. Multiple spectral indices with random forest (RF) regression were tested to estimate poppy capsule volume using an Unmanned Aircraft System (UAS). Data were collected from field-based physical measurements, in-field spectral measurements and from UAS flights with multispectral sensors over two poppy crops at Cambridge and Sorell in Tasmania, Australia. Field measured spectral signatures were convolved to the multispectral bands of a UAS mounted sensor. These convolved UAS spectral signatures were used to compute multiple spectral indices to develop the RF model, and select optimal model parameters based on root mean squared error (RMSE). In addition, the RF variable importance scores were used to rank the model variables, and to identify the best performing vegetation indices. In Cambridge, an RF model based on convolved UAS spectral signatures predicted capsule volume with an RMSE values ranging from 15.60 cm3 (10.27%) to 25.63 cm3 (14.45%) from training and validation dataset, respectively, indicating a strong relationship between SVIs and field measured capsule volume. An RF model trained on UAS multispectral data (measure not simulated) resulted an RMSE value of 19.39 cm3 (12.80%) based on training data set and an RMSE value of 26.85 cm3 (17.77%) with validation dataset. The Cambridge site model parameters and optimal variables were applied to the Sorell data, which showed a significant relationship between measured and predicted capsule volume (R2 0.72), with relative error of 26.25%. The results showed that the RF model developed using selected variables can help to predict capsule volume 2–3 weeks prior to harvest.