基于卷积神经网络的菊花花型和品种识别

菊花作为国内十大名花之一,具有极为重要的观赏价值和经济价值,表现为种类丰富、花样瓣形繁多的特点,这些特征对其智能识别和高效的管理带来很大挑战。目前菊花的识别和管理主要靠人工方式,效率不高。本文基于端到端的卷积神经网络技术,直接作用于菊花的原始图像数据,通过逐层进行特征学习,进而利用多层网络获取菊花的特征信息,从而避免了人工提取特征的困难和问题,在此基础上使用优化目标函数实现菊花花型的高效、智能识别。针对菊花花型之间差别细微的特点,在细粒度上实现区分相同花型和不同花型的目标函数,系统不仅能够识别菊花花型,还能给出菊花所属的概率值和该花型涵盖的菊花品种。系统的实现分为离线训练和在线识别2个阶段,训练处的模型可以离线托管在云端以便在移动环境下使用。为了训练网络模型,采集了大量的菊花图像样本,并手工标注了相关的花型和类别信息,在此数据集上,与现有的典型系统进行了对比试验,试验表明:系统平均识别率可以达到0.95左右,部分达到0.98,系统识别精度得到明显提升,除此之外系统还能提供更加详细的菊花种类信息,实现了的菊花花型和品种智能识别和高效管理,具有重要的理论和应用价值,为菊花的自动化管理提供了有力的手段。

Recognition for flower type and variety of chrysanthemum with convolutional neural network

Abstract: Chrysanthemum is one of the top 10 traditional famous flowers in China, which has significant importance and great ornamental value and medicinal value. The chrysanthemum flowers are characterized by a large number of varieties and a wide range of petal shapes, which pose big challenges to their intelligent identification and efficient management. Currently, the identification and management of chrysanthemum mainly relies on the traditional manual way, and as a result, the efficiency is quite low. At the contemporary era, deep learning as a powerful technique in artificial intelligence field is becoming a prevalent way of identification and classification on text, image, video, and so on. Based on the end-to-end convolutional neural network deep neural network directly acting on the original chrysanthemum image dataset, this paper aims at obtaining the characteristic information of chrysanthemums through the multi-layer neural network. By this means, the problem of extracting the features manually is avoided, and then optimization target function is applied to achieve a better image recognition accuracy. Based on this, the system of chrysanthemum flower pattern intelligent recognition and breed classification is researched and implemented. In view of the subtle differences among the flower patterns of chrysanthemums, on the one hand, in order to preserve as much information as possible for the data, tensor is employed to represent the image data; on the other hand, the pairwise confusion loss function based on pair similarity is used to distinguish pattern differences and similarities. By this means, the objective function of distinguishing the different flower patterns is realized on the fine grain size. The system not only can identify the chrysanthemum pattern, but also can give the probability value of the top 3 results. In addition to this, the variety information covered by the flower pattern is also provided. The operation of the system can be divided into 2 stages: the off-line training and the online classification. Off-line models can be hosted in the cloud environment such as Amazon AWS for the easy usage on the mobile platform. Moreover, the model can be replanted and updated with little hindrance. In order to train the network model, we collected a large amount of data of real chrysanthemum image, and manually marked the relevant pattern and category information of chrysanthemum. Based on the datasets, we conducted extensive experiments with our system and made comparisons with 3 existing systems, and experimental results show that: The identification accuracy of the system has been significantly improved compared with the existing systems for chrysanthemum flower pattern. Beyond that, the system can provide more detailed chrysanthemum species information at the same time. The average recognition rate can reach about 0.95, and even surpass the rate of 0.98 for some chrysanthemum patterns. The system provides a powerful means for the automatic management of chrysanthemum and fills the gaps in chrysanthemum pattern recognition and classification. In this paper, the research on the intelligent identification and effective management of chrysanthemum flower pattern has great significance in theory and practice.