Research on Interpretation of Ramie Lodging Information Based on Unmanned Aerial Vehicles
The most common damage to ramie tramet cultivation is stem loading.Traditional monitoring methods have drawbacks such as being time-consuming and inefficient.A method for obtaining ramie lodging information was investigated by unmanned aerial vehicles(UAV)in this study.Firstly,the canopy orthophoto and digital surface model(DSM)of ramie were created using Pix4D Mapper software.Then,the spectral,textural,and shape features of the canopy were extracted from the DSM,along with the canopy height index.Finally,a combination of 3 machine learning algorithms was used to createa classification model for normal and lodging canopies.The results showed that the DSM-based extracted plant height information could effectively replace the actual measured plant height in the field,with a model R2 of 0.899.The spectral,textural,shape,and height characteristics of fallen and normal ramets differed.The support vector machine and decision tree models outperformed the other learning algorithms,achieving 99％accuracy and efficiently identifying normal/lodging ramie plots.Above results provided technical assistance for accurate and rapid assessment of crop lodging.
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