基于超分辨率重建与机器学习的油菜苗情监测方法

为优化养分管理和确保植株正常生长，以无人机遥感技术高效且非破坏采集田间作物苗情信息，监测油菜苗期的叶面积指数（LAI）与叶绿素相对含量（SPAD）。针对无人机因飞行高度与图像分辨率相互制约，监测效率与监测精度难以兼顾的问题，采用超分辨率重建方法，融合较低飞行高度拍摄高分辨率影像，重建较高飞行高度拍摄影像，建模完成后可通过拍摄飞行影像监测LAI和SPAD。试验设置3个氮肥梯度、3个播期、3个种植密度处理，在苗期利用无人机分别采集20m及40m 2个飞行高度的油菜苗影像，采用SRRestnet方法，对40m影像进行超分辨率重建。基于20m、40m及40m重建影像中提取的3种特征组合，利用偏最小二乘回归（PLSR）、随机森林（RF）、支持向量回归（SVR）3种机器学习方法对LAI和SPAD进行监测。结果表明，超分辨率重建后的图像在表型苗情监测中表现出良好效果，PLSR监测LAI、RF监测SPAD的监测精度最高，且40m重建图像的作业效率相比于20m图像提高48.6%。

Oilseed Rape Seedling Monitoring Method Based on Super-resolution Reconstruction and Machine Learning

In order to optimize nutrient management and ensure normal plant growth, UAV remote sensing technology was used to efficiently and non-destructively collect crop seedling information in the field, and to monitor the leaf area index (LAI) and the relative chlorophyll content (SPAD) of oilseed rape during the seedling stage. It is difficult to balance the monitoring efficiency and monitoring accuracy due to the constraints of flight altitude and image resolution of UAVs. A super-resolution reconstruction method was adopted to integrate the high-resolution images taken at lower flight altitudes and reconstruct the images taken at higher flight altitudes, so that LAI and SPAD could be monitored by the flight images taken after the modeling was completed. Three nitrogen fertilizer gradients, three sowing periods, and three planting densities were set up, and the UAV was used to collect the images of oilseed rape seedlings at 20m and 40m flight altitudes respectively in seedling stage, and SRRestnet method was used to analyze the seedling images at 40m and 40m flight altitudes respectively. SRRestnet method, and super-resolution reconstruction was performed on the 40m images. Based on the three combinations of features extracted from the 20m, 40m and 40m reconstructed images, three machine learning methods, namely partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR), were utilized to monitor LAI and SPAD. The results showed that the super-resolution reconstructed images performed well in phenological seedling monitoring, and PLSR monitoring of LAI and RF monitoring of SPAD had the highest monitoring accuracy, and the operational efficiency of the 40m reconstructed images was 48.6% higher compared with that of the 20m images.