Ground coverage (GC) allows monitoring of crop growth and development and is normally estimated as the ratio of vegetation to the total pixels from nadir images captured by visible-spectrum (RGB) cameras. The accuracy of estimated GC can be significantly impacted by the effect of ‘mixed pixels’, which is related to the spatial resolution of the imagery as determined by flight altitude, camera resolution and crop characteristics (fine vs coarse textures). In this study, a two-step machine learning method was developed to improve the accuracy of GC of wheat (Triticum aestivum L.) estimated from coarse-resolution RGB images captured by an unmanned aerial vehicle (UAV) at higher altitudes. The classification tree-based per-pixel segmentation (PPS) method was first used to segment fine-resolution reference images into vegetation and background pixels. The reference and their segmented images were degraded to the target coarse spatial resolution. These degraded images were then used to generate a training dataset for a regression tree-based model to establish the sub-pixel classification (SPC) method. The newly proposed method (i.e. PPS-SPC) was evaluated with six synthetic and four real UAV image sets (SISs and RISs, respectively) with different spatial resolutions. Overall, the results demonstrated that the PPS-SPC method obtained higher accuracy of GC in both SISs and RISs comparing to PPS method, with root mean squared errors (RMSE) of less than 6% and relative RMSE (RRMSE) of less than 11% for SISs, and RMSE of less than 5% and RRMSE of less than 35% for RISs. The proposed PPS-SPC method can be potentially applied in plant breeding and precision agriculture to balance accuracy requirement and UAV flight height in the limited battery life and operation time.

地面覆盖（GC）可以监视作物的生长和发育，通常将其估计为植被与可见光谱（RGB）摄像机捕获的最低点图像中的总像素之比。估计的GC的准确性可能会受到“混合像素”的影响，这与图像的空间分辨率有关，该分辨率由飞行高度，相机分辨率和农作物特性（精细纹理与粗糙纹理）确定。在这项研究中，开发了一种两步式机器学习方法来提高小麦（Triticum aestivum）的GC准确性。L.）是根据无人机在较高高度拍摄的RGB粗分辨率图像估算的。首先使用基于分类树的每像素分割（PPS）方法将高分辨率的参考图像分割为植被和背景像素。参考及其分割的图像降级到目标粗略空间分辨率。然后将这些降级的图像用于为基于回归树的模型生成训练数据集，以建立亚像素分类（SPC ）方法。新提出的方法（即PPS-SPC）是使用六个具有不同空间分辨率的合成和四个真实UAV图像集（分别为SIS和RIS）进行评估的。总体而言，结果表明，与PPS方法相比，PPS-SPC方法在SIS和RIS中均获得了更高的GC准确度，均方根误差（RMSE）小于6％，相对RMSE（RRMSE）小于11％ SIS的RMSE小于5％，RIS的RRMSE小于35％。所提出的PPS-SPC方法可以潜在地应用于植物育种和精确农业中，以在有限的电池寿命和运行时间中平衡精确度要求和无人机飞行高度。