Flower identification based on Deep Learning

Abstract

In the field of plant scientific research, agroforestry investigation and production and management, plant identification is crucial basic work, and flower identification is an important part of plant identification. Given the present artificial defects of labor cost, low efficiency and low accuracy in present artificial flower information query and traditional computer vision method, the study built a modified tiny darknet in flowers classification method. Seventeen types of flower datasets published by Oxford University are taken as the research objects and the input of the neural network model. The deep network classification model is trained to automatically extract the characteristics of flower images. Combined with softmax classifier, the flower test images are classified and identified. The experimental results show that the classification accuracy is 92% which is higher than the classification algorithm results of the original model and some current mainstream models. This model has a simple structure, few training parameters, and has achieved a good recognition effect. It is suitable for automatic classification and recognition in the field of flower planting and is convenient for the retrieval of agricultural plant information database.