基于改进的DeepLabV3+模型结合无人机遥感的水稻倒伏识别方法

针对传统水稻倒伏监测方法以人工进行现场测量耗时耗力且受主观影响较大的问题,利用无人机成本低廉、操作简单以及分辨率高的优势,以黑龙江省佳木斯市七星农场水稻种植基地的水稻倒伏区域为研究对象,对无人机遥感图像结合改进DeepLabV3+模型的水稻倒伏识别方法进行研究.结果表明:1)与其他方法相比,改进DeepLabV3 +网...

Rice lodging recognition method based on UAV remote sensing combined with the improved DeepLabV3+ model

In response to the problem of traditional rice lodging monitoring methods are time-consuming, labor-intensive and subjectively affected by manual field measurement, the advantages of low cost, simple operation and high resolution of drones are used. The rice planting base of Qixing Farm in Jiamusi City, Heilongjiang Province is taken as the research object, and the rice lodging recognition method of the UAV remote sensing image combined with the improved DeepLabV3+ model is studied. The results show that: 1)Compared with other methods, the improved DeepLabV3+ network model achieves higher accuracy and faster recognition speed in this research method. 2)The improved DeepLabV3+ network model has an accuracy rate of 0. 99 on the test set of rice lodging images, the Kappa coefficient is 0. 98, the pixel accuracy rate is 0. 99, the recall rate is 0. 99; the balanced F score is 0. 99, the cross-combination ratio for the recognition of the complete lodging state of rice is 0. 96, and the average cross-combination ratio for the recognition of the different lodging states of the three rice varieties is 0. 97. In conclusion, the UAV equipped with RGB camera load platform to take remote sensing images combined with the improved DeepLabV3+ deep learning model can accurately identify rice lodging, providing a method for large-area, high-efficiency, low-cost rice lodging monitoring and recognition research.