摘要
为提高作物与杂草识别的准确性,结合深度卷积网络强大的特征提取能力和哈希码便于存储和快速检索的特点,提出了基于深度卷积网络和二进制哈希码的田间杂草快速识别方法。结合预训练的多层卷积网络,增加二进制哈希层构建杂草识别模型,并利用所采集的杂草数据集对模型进行fine-tuning。所提出的二进制哈希层可有效地将高维杂草特征进行压缩,以便于实际田间杂草特征的存储和后续计算。在进行杂草识别时,利用训练好的模型提取输入图像的全连接层特征码和哈希特征码,与数据库中的全连接层特征码和哈希特征码进行对比,分别计算其汉明距离与欧氏距离,找出与其最相似的K幅图像,统计这K幅图像的标签,将其归入频率最高的一类,以达到分类识别的目的。通过对比不同卷积层数和不同二进制哈希码长度对杂草识别的影响,最终确定了包含4层卷积网络和128位哈希码长度的杂草识别模型。试验结果表明,本研究方法田间杂草识别准确率可达98. 6%,并且损失函数稳定性相较于普通模型有所提高;同时,在其他杂草数据集上也有良好的表现,准确率达到95. 8%,说明该方法具有通用性。实地测试表明,利用本文提出的模型进行杂草识别,对靶喷雾杂草施药率可达92. 7%,能够有效减少农药浪费,适用于精准喷雾。
Corn is one of the main grain crops in China and its production accounts for more than 20%of the World s corn production.Weed is one of the most important factors influencing maize yield.Effective recognition method of cron and weed can improve corn quality and production accounts.At present,pesticide spraying is the main way of removing weed in China.Excessive spraying of pesticides brings problems such as environmental pollution and food safety,and therefore precise spraying is the key of weeding to reduce the amount of pesticides and increase the utilization of pesticides.Precise application of pesticides is based on accurate identification of weeds,researchers at home and abroad have done a lot of research.Most existing weed identification methods rely on manually selected weed features,such as shape,texture,etc.,which takes longer time to identify the image,and the accuracy of identification still needs further improvement.The deep learning method was used to achieve automatic extraction of weed image features without relying on artificial feature screening,and combined the binary Hash code to compress high-dimensional weed feature data to achieve rapid weed identification and provide information support for subsequent field drug spraying.In order to improve accuracy of crop and weed identification,combining with the strong feature extraction capabilities of the deep convolutional network and the ease of storage and fast retrieval of the Hash code,a fast field weed identification method was proposed based on the deep convolutional network and binary Hash code.A pre-trained multilayer convolutional neural network was used to construct a weed identification model with a binary Hash layer,and the model was fine-tuned with the collected weed data set.The binary Hash layer proposed could effectively compress the high-dimensional weed image features to facilitate the storage and subsequent calculation of the high-dimensional weed image features.During tests of weed identification,the trained model was used to extract the full-connection layer feature codes and binary Hash codes of the input image,and then compared with the full-connection layer feature codes and binary Hash codes stored in the database to calculate the Hamming distance and the Euclidean distance.After that,the most similar K images could be found out according to last step’s results.Finally,the labels’frequency of the K images was counted and the original image was classified into the highest frequency category of label to achieve the purpose of weed identification.The effects of different layers of convolutional networks and different length binary Hash code on weed identification were compared,and finally the weed identification model was determined,which included four layers convolutional neural network and 128-bit binary Hash code.The experimental results showed that the method proposed could achieve 98.6%accuracy in field weed identification,and the loss function stability was improved compared with the ordinary model.At the same time,it also performed well on other weeds datasets with an accuracy of 95.8%,which meant that the proposed method was universal.The research results could provide reference for precision weeding.The experiment carried out in corn field showed that the method could achieve 92.7%accuracy,and it could effectively reduce pesticide waste which was suitable for precision spray.
作者
姜红花
王鹏飞
张昭
毛文华
赵博
齐鹏
JIANG Honghua;WANG Pengfei;ZHANG Zhao;MAO Wenhua;ZHAO Bo;QI Peng(College of Information Science and Engineering,Shandong Agricultural University,Taian 271018,China;College of Electron and Electricity Engineering,Baoji University of Arts and Sciences,Baoji 721016,China;Chinese Academy of Agricultural Mechanization Sciences,Beijing 100083,China;Shandong Yongjia Power Co.,Ltd.,Linyi 276000,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2018年第11期30-38,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
山东省重点研发计划项目(2015GNC112004)
国家重点研发计划项目(2017YFD0700500)
山东省自然科学基金项目(ZR2018MC017)
山东农业大学智能化农业装备研发项目(24132)
关键词
杂草识别
卷积神经网络
哈希码
深度学习
特征压缩
weed identification
convolution neural network
Hash code
deep learning
feature compression
