To study the influence of foliage density and distribution on spray droplets distribution inside plant canopy, the optical porosity of vertically stratified layers of cotton canopy, was proposed to quantitatively depict the spatial distribution of cotton foliage. For calculating stratification porosity, a laser ranging bench was built to obtain ranging data for all the stratified layers. For acquiring droplet distribution data inside plant canopy, spray experiments were carried out by using artificial cotton plants as targets and water sensitive papers as a deposit sampling method. Using stratification porosities and droplets distribution data as training data, Gaussian process (GP)-based models were established to quantitatively identify droplet spatial distribution inside the canopy. The trained models predicted droplet distribution of the upper (UL) and middle layers (ML) with mean absolute percentage errors (MAPE) 0.0226, R² 0.9309 and MAPE 0.0526 and R² 0.9039, respectively. Prediction accuracy for the lower layer (LL) was MAPE 0.1181, R² 0.3361. Furthermore, for droplet distributions of the different sections in the same layer, prediction accuracies for UL and ML were obtained with MAPE 0.2038, R² 0.9114 and MAPE 0.3641, R² 0.822, respectively. For prediction accuracy of the LL was MAPE 0.2468, R² 0.4899. Considering the complex influence of all three foliage layers on droplets distribution of LL, and the horizontal velocity component of droplet movement, stratification porosity data could not explain droplets horizontal distribution in LL. Other description parameters should be included to improve model performance in future work. Moreover, the prediction model quantitatively revealed a uniform spray method could not produce a uniform deposition on the target, due to the interception of droplets by spatially inhomogeneous-distributed foliage.

为了研究叶片密度和分布对植物冠层内部液滴分布的影响，提出了垂直分层的棉花冠层的光学孔隙率，定量地描述了棉花叶片的空间分布。为了计算分层孔隙度，建立了激光测距工作台以获得所有分层层的测距数据。为了获取植物冠层内部的液滴分布数据，以人造棉植物为目标，以水敏纸为沉积物采样方法进行了喷雾实验。使用分层孔隙度和液滴分布数据作为训练数据，建立了基于高斯过程（GP）的模型，以定量识别冠层内部的液滴空间分布。MAPE）0.0226，R ² 0.9309和MAPE 0.0526和R ² 0.9039。下层（LL）的预测精度为MAPE 0.1181，R ² 0.3361。此外，对于同一层中不同部分的液滴分布，分别使用MAPE 0.2038，R ² 0.9114和MAPE 0.3641，R ² 0.822获得了UL和ML的预测精度。对于LL的预测准确度为MAPE 0.2468，R ²0.4899。考虑到所有三个叶面层对LL液滴分布的复杂影响以及液滴运动的水平速度分量，分层孔隙率数据不能解释LL中液滴的水平分布。其他描述参数应包括在内，以提高将来工作中的模型性能。此外，该预测模型定量地揭示了一种均匀的喷雾方法，由于在空间上分布不均匀的树叶拦截了液滴，因此无法在目标上产生均匀的沉积。