.png){kind=icon}
返回
Leaf image based cucumber disease recognition using sparse representation classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compag.2017.01.014.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Cucumber diseased leaf image
Cucumber disease recognition
Sparse representation classification (SRC)
Sparse coefficient
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
1.0W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
分享收藏
Influence of olive and rosemary leaves extracts on chemically induced liver cirrhosis in male rats
OAAI
OAAI分享收藏分享收藏Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance基于高光谱的支持向量机植物病害早期检测与分类
PREAI
分享收藏
查看更多内容