Distribution of Weeds in Soybean Fields Based on Improved YOLO v5
In order to realize the timely and accurate estimation of the number and area of weeds in soybean fields,a method for identifying weeds in soybean fields based on improved YOLO v5 was proposed.Taking weeds in soybean fields in natural scenes as the research object,the image data was acquired and enhanced by UAV.By introducing the adaptive feature fusion mechanism to build a detection model,based on the measured data to establish a linear regression model be-tween the measured quantity and area and the estimated quantity and area,and established the distribution map of farm-land weeds.The results of weed target extraction by comparing different methods showed that the improved YOLO v5-AS-FF model was better than YOLO v5l,YOLO v5s and YOLO v5x,with F1 value of 0.903 and recognition speed of 0.268 s/piece.The correlation coefficient R2 of the linear regression model between the measured and estimated weed number and area was 0.973 0,with a high fitting degree.The method has low error,can quickly and accurately identify the number of soybean seedlings,and can provide support for grass condition judgment in the field.
soybeanweed identificationunmannd aerial vehicleYOLOdeep learningdistribution of weeds
国家科技期刊平台
NSTL
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