Feature Recognition of Crop Growth Information in Precision Farming
Joint Authors
Sun, Hanqing
Zhang, Xiaohui
Yu, Zhou
Xi, Gang
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-15
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed.
The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features.
Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type.
Finally, the classification method of BP neural network is used to classify the obtained feature vectors.
The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.
American Psychological Association (APA)
Sun, Hanqing& Zhang, Xiaohui& Yu, Zhou& Xi, Gang. 2018. Feature Recognition of Crop Growth Information in Precision Farming. Complexity،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1136602
Modern Language Association (MLA)
Sun, Hanqing…[et al.]. Feature Recognition of Crop Growth Information in Precision Farming. Complexity No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1136602
American Medical Association (AMA)
Sun, Hanqing& Zhang, Xiaohui& Yu, Zhou& Xi, Gang. Feature Recognition of Crop Growth Information in Precision Farming. Complexity. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1136602
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136602