Lodging is a major limiting factor for the yield, quality and harvesting efficiency of selected crops worldwide. This study presents an efficient, robust and non-destructive assessment of lodging severity for four different grasses for seed production, using images collected by an unoccupied aerial vehicle (UAV) in two field plot experiments across five growing seasons. Canopy texture and height related features were extracted from individual plot images and evaluated for estimating lodging severity. Histograms of oriented gradients (HOG) were used as texture features, and three canopy height distributions features (CHV1, CHV2 and CHV3) were proposed. Each canopy height distribution feature divides the plots into subplots and estimates the average height of each subplot. CHV1 concatenates average height of the subplots into its feature, while CHV2 concatenates the difference in average height between all subplots, and CHV3 concatenates the difference in average height between adjacent subplots. The plots were classified using support vector machines into three categories according to the lodging severity. The results showed that the HOG and height distribution features can be used for grading lodging severity in UAV images with high accuracy (71.9% and 79.1%, respectively). However, the HOG features showed a negative relationship to the ground sample distance (GSD), while the CHV1 had a constant accuracy across the GSDs. Combination of the two features did not significantly improve the classification accuracy. The present results have potential to generate lodging severity maps for application in precision farming and thereby to increase grass seed yield and harvest efficiency at farm scale. It should be noted that results and methods from the current study might not be transferred to other crops due to crop specific lodging characteristics and effect of yields.

倒伏是世界范围内选定作物的产量、质量和收获效率的主要限制因素。本研究使用无人飞行器 (UAV) 在五个生长季节的两个田间试验中收集的图像，对用于种子生产的四种不同草的倒伏严重程度进行了有效、稳健和非破坏性的评估。从个别地块图像中提取冠层纹理和高度相关特征，并评估倒伏严重程度。定向梯度直方图（HOG）被用作纹理特征，并提出了三种冠层高度分布特征（CHV1、CHV2和CHV3）。每个冠层高度分布特征将地块划分为子地块并估计每个子地块的平均高度。CHV1 将子图的平均高度连接到其特征中，而 CHV2 连接所有子图之间的平均高度差异，而 CHV3 连接相邻子图之间的平均高度差异。根据倒伏严重程度，使用支持向量机将地块分为三类。结果表明，HOG 和高度分布特征可用于对无人机图像中的倒伏严重程度进行分级，准确率分别为 71.9% 和 79.1%。然而，HOG 特征与地面样本距离 (GSD) 呈负相关，而 CHV1 在整个 GSD 中具有恒定的精度。两个特征的结合并没有显着提高分类精度。目前的结果有可能生成倒伏严重性地图，用于精准农业，从而提高农场规模的草种产量和收获效率。应该注意的是，由于作物特定的倒伏特性和产量的影响，当前研究的结果和方法可能不会转移到其他作物上。