Big Data of Rhizome Chinese Herbal Medicine Harvester from the Perspective of Internet

As the national and local governments attach great importance to bio medicine and health industry, the planting area of rhizome Chinese herbal medicine is increasing year by year. Therefore, the harvest of rhizome Chinese herbal medicine has become an urgent problem for farmers and enterprises planting rhizomatous Chinese herbal medicine. This paper focuses on the big data research of rhizome traditional Chinese medicine harvesting machine based on Internet time domain. In this paper, through the study of image recognition under big data, the image recognition of rhizome traditional Chinese medicine is carried out. The side texture features of medicinal materials are extracted by gray level co-occurrence matrix method. Combined with HSV and color component feature value in color space, BP neural network is used for pattern recognition to realize the identification of medicinal materials. The results show that the recognition rate of BP neural network pattern recognition based on color and shape features of cross section of medicinal materials is 94%, which can drive the development of image recognition of Rhizomatous Chinese herbal medicine harvesting machinery.