期刊
COMPUTERS AND ELECTRONICS IN AGRICULTURE
卷 134, 期 -, 页码 135-141出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compag.2017.01.014
关键词
Cucumber diseased leaf image; Cucumber disease recognition; Sparse representation classification (SRC); Sparse coefficient
资金
- China National Natural Science Foundation [61473237, 61309008]
- Shaanxi Natural Science Foundation Research Project [2014JM2-6096]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据