Flower counting for typical plants in desert steppe using deep learning method
Desert steppe plant flower counting can help us understand the growth status,reproductive capacity,community structure and other information of plants.It is a time-consuming and labor-intensive process to obtain the flower abundance of different species in grassland by manual counting.Based on the deep learning target detection method,this study trained detection model,and then evaluated and applicated on 10 plots with 50 quadrats in the desert steppe.From the overall performance of the three models of YOLOv7,the results of YOLOv7-E6E model were very good for some types of flowers,with F1-sorce and mAP@0.5 higher than 0.7.The performance of the 3 models of YOLOv7 in the detection of 5 kinds of flowers,the mAP@0.5 of the YOLOv7-X and YOLOv7-E6E models is higher than 0.8 in the detection of Haplophyllum dauricum,Allium mongolicum,and Allium tenuissimum,while only the mAP@0.5 of YOLOv7-E6E in Tribulus terrestris and Lagochilus ilicifolius exceeds 0.6.Application of the model in flower counting of 50 quadrats,the overall accuracy rate of flower counting of the YOLOv7-E6E model is 0.91,which can meet the needs of detecting and counting these 5 grassland flowering plants.To sum up,the rapid counting of flowers by deep learning can improve the survey efficiency of flowering plants at the quadrat scale.However,in order to meet the task requirements of large-scale species survey and counting,it is still necessary to expand the sample size and continuously improve the model structure to improve the overall performance of flower detection in model plants.
Deep learningobject detectionflower countinggrassland plant
NETL
NSTL
万方数据
