农业机械学报2024，Vol.55Issue(6)：196-201,6.DOI:10.6041/j.issn.1000-1298.2024.06.020
基于超分辨率重建与机器学习的油菜苗情监测方法
Oilseed Rape Seedling Monitoring Method Based on Super-resolution Reconstruction and Machine Learning
摘要
Abstract
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 20 m and 40 m flight altitudes respectively in seedling stage,and SRRestnet method was used to analyze the seedling images at 40 m and 40 m flight altitudes respectively.SRRestnet method,and super-resolution reconstruction was performed on the 40 m images.Based on the three combinations of features extracted from the 20 m,40 m and 40 m 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 40 m reconstructed images was 48.6％higher compared with that of the 20 m images.关键词
油菜/苗情监测/叶面积指数/叶绿素相对含量/超分辨率重建/机器学习Key words
oilseed rape/seedling monitoring/leaf area index/relative chlorophyll content/super-resolution reconstruction/machine learning分类
农业科技引用本文复制引用
杨扬,刘洋,苏宸,赵杰,张强强,周广生..基于超分辨率重建与机器学习的油菜苗情监测方法[J].农业机械学报,2024,55(6):196-201,6.基金项目
国家重点研发计划项目(2021YFD1600502) （2021YFD1600502）
