INTELLIGENT ACQUISITION METHOD OF HERBACEOUS FLOWERS IMAGE BASED ON THEME CRAWLER, DEEP LEARNING AND GAME THEORY

Abstract

In order to obtain a large amount of training data in a short time, flower classification is carried out through image processing and deep learning algorithms based on game theory and optimization. The Python web crawler technology is used to write the image and short video crawler programs based on the Chinese name of herbal flowers, and the target detection model is used to screen the flower image on the basis of the static frame of the segmented video, so as to improve the speed and accuracy of image acquisition. The result show that the use of theme crawler technology can obtain the image of herbaceous flowers effectively; target detection can greatly improve the image utilization, the number of samples can be increased by 3~10 times, and the average error detection rate is only 3.62%; the GAN (GenerativeAdversarial Network) is a deep learning model based on game theory. GAN model can generate realistic flower pictures, which provides a new research idea to solve the problem of lack of agriculture data set at present, and shows the feasibility of intelligent data collection method for herbaceous flowers.