Sample 360 video for the analysis of plant movement

Violent movement of cereal crop stems can lead to failure under high winds. Known as lodging, this phenomenon is particularly severe in cereal crops such as oat, barley, and wheat, and contributes to yield and economic losses. Quantifying the movement of cereal crops under field wind stress could aid in the breeding and selecting of lodging resistant cereals. We present a method to quantify the wave like movement of cereal crop rows in a high throughput fashion under field wind conditions. By analyzing pre-defined regions of hemispherical 4K resolution video, we obtain a time varying color signal of wind induced stem and canopy movement. Bandpass filtering is applied to the color signals to filter out changes in lighting due to sunlight changes, enabling comparisons across different lighting conditions. Peaks are then identified in the signal, and the distance in frames to the next peak as well as the absolute area under the curve between peaks is recorded. The distributions of distances to adjacent peaks (expressed as frequencies) are recorded and the area within a defined frequency bin is summed to get an approximation of the frequency and amount movement. We applied this method to analyze the wind induced movement of 16 cereal cultivars planted in a randomized complete block design on 5 different windy days. We detected significant differences in the mean frequency and amplitude within 0.2 Hz frequency bins among 16 cereal cultivars, with mean frequencies ranging between 1.24 and 1.53 Hz. This method quantifies the frequency and amplitude of movement in cereal varieties at high throughput in the field, and shows promise for characterizing the physiological basis for differences in cereal movement and lodging resistance.强风环境下，谷类作物茎秆的剧烈晃动可引发植株倒伏。该现象被称为倒伏（lodging），在燕麦、大麦、小麦等谷类作物中尤为严重，会造成产量与经济损失。量化田间风力胁迫下谷类作物的晃动情况，有助于培育并筛选抗倒伏谷类品种。本研究提出一种方法，可在田间风力条件下以高通量方式量化谷类作物株行的类波浪晃动。通过对半球形4K分辨率视频的预设区域进行分析，我们可获取由风力诱导的茎秆与冠层晃动的时变颜色信号。对该颜色信号应用带通滤波，以滤除由日光变化引发的光照改变，从而实现不同光照条件下的结果对比。随后识别信号中的峰值，记录相邻峰值间的帧间隔，以及峰值间曲线下的绝对面积。记录相邻峰值间隔的分布（以频率形式表示），并对指定频率区间内的面积求和，以近似估算晃动的频率与幅度。本研究将该方法应用于分析16个谷类品种的风力诱导晃动情况，这些品种采用随机完全区组设计种植，实验共开展于5个大风天。在0.2 Hz的频率区间内，我们检测到16个谷类品种的平均频率与幅度存在显著差异，其平均频率介于1.24至1.53 Hz之间。该方法可在田间以高通量方式量化谷类品种的晃动频率与幅度，有望为解析谷类作物晃动与抗倒伏性差异的生理基础提供支撑。