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.

改善罂粟荚果体积的预测对于优化罂粟作物的管理至关重要。为了使用遥感图像准确估计罂粟胶囊的体积，选择最合适的模型和预测变量至关重要。使用无人飞机系统（UAS）对​​具有随机森林（RF）回归的多个光谱指数进行了测试，以估算罂粟胶囊的体积。数据是通过基于田间的物理测量，现场光谱测量以及在澳大利亚塔斯马尼亚州Cambridge和Sorell的两种罂粟作物上使用多光谱传感器的UAS飞行收集的。现场测量的光谱特征被卷积到安装了UAS的传感器的多光谱波段上。这些卷积的UAS光谱特征用于计算多个光谱指数以开发RF模型，并根据均方根误差（RMSE）选择最佳模型参数。此外，RF变量重要性评分用于对模型变量进行排名，并确定性能最佳的植被指数。在剑桥，基于卷积UAS频谱特征的RF模型预测的胶囊体积的RMSE值在15.60 cm范围内分别来自训练和验证数据集的³（10.27％）至25.63 cm ³（14.45％），表明SVI与现场测得的胶囊体积之间有很强的关系。基于UAS多光谱数据训练的RF模型（未模拟测量）基于训练数据集获得了19.39 cm ³（12.80％）的RMSE值，并通过验证数据集获得了26.85 cm ³（17.77％）的RMSE值。将剑桥站点模型参数和最佳变量应用于Sorell数据，该数据显示实测胶囊体积与预测胶囊体积之间存在显着关系（R ² 0.72），相对误差为26.25％。结果表明，使用选定变量开发的RF模型可以帮助预测收获前2-3周的胶囊体积。