摘要
针对花卉养护过程中,由于人们缺乏专业的养护技术,花卉的养护难度大、成本高的问题,本文提出了一种基于深度学习的图像分类技术实现花卉的全自动化养护方法.因花卉的生长状况往往受诸多因素影响,仅依靠对花卉生长状况图像的分类容易对花卉的生长状况作出错误的判断,因此该方法设计了一种由花卉图像特征和生长环境参数组成的两个输入通道的卷积神经网络实现花卉生长状态的自动识别.实验表明,该方法可以提高花卉生长状况的识别准确率,从而可提高花卉的自动化养护技术水平.
In the process of flower conservation,due to the lack of professional maintenance technology,the difficulty of flower conservation and high cost,this study proposes a method based on deep learning image classification technology to realize the full automatic flower maintenance.Because the growth of flowers is often affected by many factors,it is easy to make a wrong judgment on the growth of flowers by relying on the classification of flower growth status images.Therefore,this method designs two kinds of flower image features and growth environment parameters.The convolutional neural network of the input channel automatically recognizes the state of flower growth.Experiments show that this method can improve the recognition accuracy of flower growth status,and thus improve the level of automatic maintenance technology of flowers.
作者
黄宏梅
陆卫忠
杨茹
曹燕
HUANG Hong-Mei;LU Wei-Zhong;YANG Ru;CAO Yan(College of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou 215009,China;Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency,Suzhou 215009,China)
出处
《计算机系统应用》
2020年第3期261-268,共8页
Computer Systems & Applications
基金
国家自然科学基金(61672371)
江苏省教育厅自然科学研究项目(08KJD510007)。
关键词
智能花卉
深度学习
卷积神经网络
图像识别
智能控制
intelligent flower
deep learning
convolutional neural network
image recognition
intelligent control
