Estimation of No-Tillage Planting Summer Maize Seedling Number Based on UAV Visible Light Images
This study aimed to quickly estimate the emergence number of no-tillage planting summer maize and improve the field management accuracy.Using the high-resolution visible light images of summer maize fields obtained by UAV equipped with a visible light camera,8 vegetation indices were calculated and used to segment vegetation and non-vegetation combined with the maximum between-class variance method.After comparing the segment effect,the red vegetation index(RI)binary image was selected for the visible light image mask.Through counting of summer corn and weeds,comparing the variation coefficients of 24 tex-ture features of weeds and their relative difference coefficient with those of summer maize,the red variance was selected as the feature to extract summer maize seedlings,and the threshold determined by time series inter-section threshold method was used to remove weed.With the extracted morphological characteristic parameters of summer maize seedlings,four algorithms of support vector machine(SVM),BP neural network,K-nearest neighbor and decision tree were used to construct the prediction model of summer maize seedling number.The results showed that the overall effects of SVM and decision tree algorithm were better with determination coeffi-cient over 0.8 and mean absolute error less than 0.3,and the accuracy of decision tree model was the highest up to 94.1% .The results of this study could provide technical support for estimating the emergence rate of summer maize in large area.
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