Citrus recognition based on YOLOv4 neural network

Abstract

Abstract In order to realize the accurate and rapid recognition of citrus fruit by autonomous picking robot in natural environment, a citrus recognition method based on YOLOv4 neural network is proposed. This algorithm improves the YOLOv4 model and uses the Kmeans++ algorithm to obtain the prior frame in the model to enhance the scale adaptability. Take citrus pictures independently and expand the data, use the LableImage platform to mark the data, and train the network model under the Darknet framework. According to the results, the citrus recognition model has good robustness and real-time performance to the common interference factors and their superposition in natural picking environment. The average recognition accuracy is 89.23 and the average detection speed is 60 ms. The average recognition accuracy is 89.23. It meets the requirements of real-time image recognition speed and accuracy of citrus picking robot.