安徽大学学报（自然科学版）2019，Vol.43Issue(2)：44-53,10.DOI:10.3969/j.issn.1000-2162.2019.02.007
融合卷积神经网络与Adaboost算法的病害松树识别
Identification of diseased pine trees by fusion convolutional neural network and Adaboost algorithm
摘要
Abstract
Aiming at the high-resolution visible pine tree image acquired by the UAV platform, a method of disease pine identification combined with deep convolutional neural network and Adaboost algorithm was proposed to solve the problem of low accuracy of traditional machine learning method for identifying diseased pine. Firstly, the convolutional neural network was used to train the diseased pine model and then used the pre-training model to remove the complex information such as fields, bare soil and shadows, and extracted the color and texture features of the diseased pine, healthy pine and shadow areas. The Adaboost classifier was used to identify the disease target according to the extracted features in the decision-making layer after the object interference item was removed. The experimental results showed that the proposed method had significantly higher recognition accuracy than traditional K-means clustering, support vector machine, Adaboost algorithm, BP neural network and VGG (visual geometry group) algorithm.关键词
深度卷积神经网络/Adaboost算法/机器学习/目标识别/支持向量机Key words
deep convolutional neural network/adaboost algorithm/machine learning/target recognition/support vector machine分类
信息技术与安全科学引用本文复制引用
胡根生,殷存军,张艳,方怡,朱艳秋..融合卷积神经网络与Adaboost算法的病害松树识别[J].安徽大学学报（自然科学版）,2019,43(2):44-53,10.基金项目
国家自然科学基金资助项目 (61672032) （61672032）
偏振光成像探测技术安徽省重点实验室开放课题 (2016-KFKT-003) （2016-KFKT-003）
