关键词: 深度卷积神经网络, 迁移学习, 植物识别, 多线索

Abstract:

Most existing research on automated plant identification has focused on identifying single organs of plants, such as flowers, leaves or fruits. Plant identification using a single organ is not reliable because many different plants have very similar organs. For pictures taken directly in the wild, there are usually complex backgrounds, which is another reason why the accuracy of plant image recognition is not high. In order to overcome these two problems in image recognition, this paper proposes a multi-cue plant recognition. Specifically, a Deep Convolutional Neural Network（DCNN） is used to learn the single organ classifiers of flowers, fruits, leaves, and whole plants by transfer learning, and finally multi-organ fusion recognition is done based on the labels and scores predicted by each classifier. This paper demonstrates the efficiency of the model on the PlantCLEF2017 dataset, and the plant recognition performance has been greatly improved.

Key words: Deep Convolutional Neural Networks（DCNN）, transfer learning, plant identification, multi-cue